Changeset 255 for trunk/VUT


Ignore:
Timestamp:
08/28/05 15:12:11 (19 years ago)
Author:
bittner
Message:
 
Location:
trunk/VUT/doc/SciReport
Files:
7 edited

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  • trunk/VUT/doc/SciReport/analysis.tex

    r249 r255  
    11\chapter{Analysis of Visibility in Polygonal Scenes} 
    22 
    3  
    4 \section{Related work} 
    5 \label{VFR3D_RELATED_WORK} 
    6  
    7  
    8  Below we briefly discuss the related work on visibility preprocessing 
    9  in several application areas. 
    10  
    11  
    12 \subsection{Aspect graph} 
    13  
    14 The first algorithms dealing with from-region visibility belong to the 
    15 area of computer vision. The {\em aspect 
    16 graph}~\cite{Gigus90,Plantinga:1990:RTH, Sojka:1995:AGT} partitions 
    17 the view space into cells that group viewpoints from which the 
    18 projection of the scene is qualitatively equivalent. The aspect graph 
    19 is a graph describing the view of the scene (aspect) for each cell of 
    20 the partitioning. The major drawback of this approach is that for 
    21 polygonal scenes with $n$ polygons there can be $\Theta(n^9)$ cells in 
    22 the partitioning for unrestricted viewspace. A {\em scale space} 
    23 aspect graph~\cite{bb12595,bb12590} improves robustness of the method 
    24 by merging similar features according to the given scale. 
    25  
    26  
    27 \subsection{Potentially visible sets} 
    28  
    29  
    30  In the computer graphics community Airey~\cite{Airey90} introduced 
    31 the concept of {\em potentially visible sets} (PVS).  Airey assumes 
    32 the existence of a natural subdivision of the environment into 
    33 cells. For models of building interiors these cells roughly correspond 
    34 to rooms and corridors.  For each cell the PVS is formed by cells 
    35 visible from any point of that cell.  Airey uses ray shooting to 
    36 approximate visibility between cells of the subdivision and so the 
    37 computed PVS is not conservative. 
    38  
    39 This concept was further elaborated by Teller et 
    40 al.~\cite{Teller92phd,Teller:1991:VPI} to establish a conservative 
    41 PVS.  The PVS is constructed by testing the existence of a stabbing 
    42 line through a sequence of polygonal portals between cells. Teller 
    43 proposed an exact solution to this problem using \plucker 
    44 coordinates~\cite{Teller:1992:CAA} and a simpler and more robust 
    45 conservative solution~\cite{Teller92phd}.  The portal based methods 
    46 are well suited to static densely occluded environments with a 
    47 particular structure.  For less structured models they can face a 
    48 combinatorial explosion of complexity~\cite{Teller92phd}. Yagel and 
    49 Ray~\cite{Yagel95a} present an algorithm, that uses a regular spatial 
    50 subdivision. Their approach is not sensitive to the structure of the 
    51 model in terms of complexity, but its efficiency is altered by the 
    52 discrete representation of the scene. 
    53  
    54 Plantinga proposed a PVS algorithm based on a conservative viewspace 
    55 partitioning by evaluating visual 
    56 events~\cite{Plantinga:1993:CVP}. The construction of viewspace 
    57 partitioning was further studied by Chrysanthou et 
    58 al.~\cite{Chrysanthou:1998:VP}, Cohen-Or et al.~\cite{cohen-egc-98} 
    59 and Sadagic~\cite{Sadagic}.  Sudarsky and 
    60 Gotsman~\cite{Sudarsky:1996:OVA} proposed an output-sensitive 
    61 visibility algorithm for dynamic scenes.  Cohen-Or et 
    62 al.~\cite{COZ-gi98} developed a conservative algorithm determining 
    63 visibility of an $\epsilon$-neighborhood of a given viewpoint that was 
    64 used for network based walkthroughs. 
    65  
    66 Conservative algorithms for computing PVS developed by Durand et 
    67 al.~\cite{EVL-2000-60} and Schaufler et al.~\cite{EVL-2000-59}  make 
    68 use of several simplifying assumptions to avoid the usage of 4D data 
    69 structures.  Wang et al.~\cite{Wang98} proposed an algorithm that 
    70 precomputes visibility within beams originating from the restricted 
    71 viewpoint region. The approach is very similar to the 5D subdivision 
    72 for ray tracing~\cite{Simiakakis:1994:FAS} and so it exhibits similar 
    73 problems, namely inadequate memory and preprocessing complexities. 
    74 Specialized algorithms for computing PVS in \m25d scenes were proposed 
    75 by Wonka et al.~\cite{wonka00}, Koltun et al.~\cite{koltun01}, and 
    76 Bittner et al.~\cite{bittner:2001:PG}. 
    77  
    78 The method presented in the thesis was first outlined 
    79 in~\cite{bittner99min}. Recently, a similar exact algorithm for PVS 
    80 computation was developed by Nirenstein et 
    81 al.~\cite{nirenstein:02:egwr}. This algorithm uses \plucker 
    82 coordinates to compute visibility in shafts defined by each polygon in 
    83 the scene. 
    84  
    85  
    86 \subsection{Rendering of shadows} 
    87  
    88  
    89 The from-region visibility problems include the computation of soft 
    90 shadows due to an areal light source. Continuous algorithms for 
    91 real-time soft shadow generation were studied by Chin and 
    92 Feiner~\cite{Chin:1992:FOP}, Loscos and 
    93 Drettakis~\cite{Loscos:1997:IHS}, and 
    94 Chrysanthou~\cite{Chrysantho1996a} and Chrysanthou and 
    95 Slater~\cite{Chrysanthou:1997:IUS}. Discrete solutions have been 
    96 proposed by Nishita~\cite{Nishita85}, Brotman and 
    97 Badler~\cite{Brotman:1984:GSS}, and Soler and Sillion~\cite{SS98}. An 
    98 exact algorithm computing an antipenumbra of an areal light source was 
    99 developed by Teller~\cite{Teller:1992:CAA}. 
    100  
    101  
    102 \subsection{Discontinuity meshing} 
    103  
    104  
    105 Discontinuity meshing is used in the context of the radiosity global 
    106 illumination algorithm or computing soft shadows due to areal light 
    107 sources.  First approximate discontinuity meshing algorithms were 
    108 studied by Campbell~\cite{Campbell:1990:AMG, Campbell91}, 
    109 Lischinski~\cite{lischinski92a}, and Heckbert~\cite{Heckbert92discon}. 
    110 More elaborate methods were developed by 
    111 Drettakis~\cite{Drettakis94-SSRII, Drettakis94-FSAAL}, and Stewart and 
    112 Ghali~\cite{Stewart93-OSACS, Stewart:1994:FCSb}. These methods are 
    113 capable of creating a complete discontinuity mesh that encodes all 
    114 visual events involving the light source. 
    115  
    116 The classical radiosity is based on an evaluation of form factors 
    117 between two patches~\cite{Schroder:1993:FFB}. The visibility 
    118 computation is a crucial step in the form factor 
    119 evaluation~\cite{Teller:1993:GVA,Haines94,Teller:1994:POL, 
    120 Nechvile:1996:FFE,Teichmann:WV}. Similar visibility computation takes 
    121 place in the scope of hierarchical radiosity 
    122 algorithms~\cite{Soler:1996:AEB, Drettakis:1997:IUG, Daubert:1997:HLS}. 
    123  
    124  
    125  
    126 \subsection{Global visibility} 
    127  
    128  The aim of {\em global visibility} computations is to capture and 
    129 describe visibility in the whole scene~\cite{Durand:1996:VCN}. The 
    130 global visibility algorithms are typically based on some form of {\em 
    131 line space subdivision} that partitions lines or rays into equivalence 
    132 classes according to their visibility classification. Each class 
    133 corresponds to a continuous set of rays with a common visibility 
    134 classification. The techniques differ mainly in the way how the line 
    135 space subdivision is computed and maintained. A practical application 
    136 of most of the proposed global visibility structures for 3D scenes is 
    137 still an open problem.  Prospectively these techniques provide an 
    138 elegant method for ray shooting acceleration --- the ray shooting 
    139 problem can be reduced to a point location in the line space 
    140 subdivision. 
    141  
    142  
    143 Pocchiola and Vegter introduced the visibility complex~\cite{pv-vc-93} 
    144 that describes global visibility in 2D scenes. The visibility complex 
    145 has been applied to solve various 2D visibility 
    146 problems~\cite{r-tsvcp-95,r-wvcav-97, r-dvpsv-97,Orti96-UVCRC}.  The 
    147 approach was generalized to 3D by Durand et 
    148 al.~\cite{Durand:1996:VCN}. Nevertheless, no implementation of the 3D 
    149 visibility complex is currently known. Durand et 
    150 al.~\cite{Durand:1997:VSP} introduced the {\em visibility skeleton} 
    151 that is a graph describing a skeleton of the 3D visibility 
    152 complex. The visibility skeleton was verified experimentally and  the 
    153 results indicate that its $O(n^4\log n)$ worst case complexity is much 
    154 better in practice. Pu~\cite{Pu98-DSGIV} developed a similar method to 
    155 the one presented in this chapter. He uses a BSP tree in \plucker 
    156 coordinates to represent a global visibility map for a given set of 
    157 polygons. The computation is performed considering all rays piercing 
    158 the scene and so the method exhibits unacceptable memory complexity 
    159 even for scenes of moderate size. Recently, Duguet and 
    160 Drettakis~\cite{duguet:02:sig} developed a robust variant of the 
    161 visibility skeleton algorithm that uses robust epsilon-visibility 
    162 predicates. 
    163  
    164  Discrete methods aiming to describe visibility in a 4D data structure 
    165 were presented by Chrysanthou et al.~\cite{chrysanthou:cgi:98} and 
    166 Blais and Poulin~\cite{blais98a}.  These data structures are closely 
    167 related to the {\em lumigraph}~\cite{Gortler:1996:L,buehler2001} or 
    168 {\em light field}~\cite{Levoy:1996:LFR}. An interesting discrete 
    169 hierarchical visibility algorithm for two-dimensional scenes was 
    170 developed by Hinkenjann and M\"uller~\cite{EVL-1996-10}. One of the 
    171 biggest problems of the discrete solution space data structures is 
    172 their memory consumption required to achieve a reasonable 
    173 accuracy. Prospectively, the scene complexity 
    174 measures~\cite{Cazals:3204:1997} provide a useful estimate on the 
    175 required sampling density and the size of the solution space data 
    176 structure. 
    177  
    178  
    179 \subsection{Other applications} 
    180  
    181  Certain from-point visibility problems determining visibility over a 
    182 period of time can be transformed to a static from-region visibility 
    183 problem. Such a transformation is particularly useful for antialiasing 
    184 purposes~\cite{grant85a}. The from-region visibility can also be used 
    185 in the context of simulation of the sound 
    186 propagation~\cite{Funkhouser98}. The sound propagation algorithms 
    187 typically require lower resolution than the algorithms simulating the 
    188 propagation of light, but they need to account for simulation of 
    189 attenuation, reflection and time delays. 
    1903 
    1914 

  • trunk/VUT/doc/SciReport/code/c2tex.cpp

    r243 r255  
    2222#define S_LEN   256 
    2323 
    24 int pascal=0; 
     24int usePascal=0; 
    2525 
    2626char comment_1b='/',comment_2b='*',comment_1e='*',comment_2e='/'; 
    … …  
    475475void Help() 
    476476{ 
    477   printf("Syntax : c2tex in_file [out_file] [-pascal] [-numbers]\n\n"); 
     477  printf("Syntax : c2tex in_file [out_file] [-usePascal] [-numbers]\n\n"); 
    478478  printf("Default out_file is output.tex.\n"); 
    479   printf("-pascal     use pascal comments (* *).\n"); 
     479  printf("-usePascal     use usePascal comments (* *).\n"); 
    480480  printf("-numbers    print line numbers.\n"); 
    481481  exit(1); 
    … …  
    494494   strcpy(s,"output.tex"); 
    495495  
    496  if (options.isOption("-pascal")) { 
    497     pascal=1; 
     496 if (options.isOption("-usePascal")) { 
     497    usePascal=1; 
    498498    comment_1b='('; 
    499499    comment_2b='*'; 

  • trunk/VUT/doc/SciReport/code/c2tex.tex

    r251 r255  
    1313\leftline{11:\ \ \  } 
    1414\leftline{12:\ \ \  } 
    15 \keyb{}\leftline{13:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}KEYWORD1\symbol{}\ \ \ \ \ \ \ \normal{}1\symbol{}\ \ \ \ \ \comment{}//\ kody\ pro\ jednotlive\ znacky } 
     15\keyb{}\leftline{13:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}KEYWORD1\symbol{}\ \ \ \ \ \ \ \normal{}1\symbol{}\ \ \ \ \ \symbol{}/\normal{}ednotlive\symbol{}\ \normal{}znacky\symbol{} } 
    1616\keyb{}\leftline{14:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}KEYWORD2\symbol{}\ \ \ \ \ \ \ \normal{}2\symbol{} } 
    1717\keyb{}\leftline{15:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}SYMBOL\symbol{}\ \ \ \ \ \ \ \ \ \normal{}3\symbol{} } 
    … …  
    2323\keyb{}\leftline{21:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}S_LEN\symbol{}\ \ \normal{}256\symbol{} } 
    2424\leftline{22:\ \ \  } 
    25 \keya{}\leftline{23:\ \ \ int\symbol{}\ \keya{}pascal\symbol{}=\normal{}0\symbol{}; } 
     25\keya{}\leftline{23:\ \ \ int\symbol{}\ \normal{}usePascal\symbol{}=\normal{}0\symbol{}; } 
    2626\leftline{24:\ \ \  } 
    2727\keya{}\leftline{25:\ \ \ char\symbol{}\ \normal{}comment_1b\symbol{}='/',\normal{}comment_2b\symbol{}='*',\normal{}comment_1e\symbol{}='*',\normal{}comment_2e\symbol{}='/'; } 
    … …  
    4343\leftline{41:\ \ \ \ \ \keya{}char\symbol{}\ **\normal{}strings\symbol{}; } 
    4444\leftline{42:\ \ \ \ \  } 
    45 \comment{}\leftline{43:\ \ \ //\ \ OptionsC()\ $\{$\ number=0;\ $\}$ } 
    46 \symbol{}\leftline{44:\ \ \ \ \ \normal{}OptionsC\symbol{}(\keya{}int\symbol{}\ \normal{}n\symbol{},\keya{}char\symbol{}\ **\normal{}a\symbol{})\ $\{$\ \normal{}init\symbol{}(\normal{}n\symbol{},\normal{}a\symbol{});\ $\}$ } 
    47 \leftline{45:\ \ \ } 
    48 \leftline{46:\ \ \ \ \ \keya{}void\symbol{}\ \normal{}init\symbol{}(\keya{}int\symbol{}\ \normal{}n\symbol{},\keya{}char\symbol{}\ **\normal{}s\symbol{})\ $\{$\ \normal{}number\symbol{}=\normal{}n\symbol{};\ \normal{}strings\symbol{}=\normal{}s\symbol{};\ $\}$ } 
    49 \leftline{47:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
    50 \leftline{48:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\keya{}int\symbol{}\ *\normal{}number\symbol{}); } 
    51 \leftline{49:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getParam\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ \normal{}n\symbol{}=\normal{}1\symbol{}); } 
    52 \leftline{50:\ \ \ } 
    53 \leftline{51:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\keya{}int\symbol{}\ *\normal{}result\symbol{}); } 
    54 \leftline{52:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\normal{}Real\symbol{}\ *\normal{}r\symbol{}); } 
    55 \leftline{53:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\keya{}char\symbol{}\ *\normal{}result\symbol{}); } 
    56 \leftline{54:\ \ \ } 
    57 \leftline{55:\ \ \ $\}$; } 
     45\symbol{}\leftline{43:\ \ \ /\normal{}OptionsC\symbol{}(\keya{}int\symbol{}\ \normal{}n\symbol{},\keya{}char\symbol{}\ **\normal{}a\symbol{})\ $\{$\ \normal{}init\symbol{}(\normal{}n\symbol{},\normal{}a\symbol{});\ $\}$ } 
     46\leftline{44:\ \ \ } 
     47\leftline{45:\ \ \ \ \ \keya{}void\symbol{}\ \normal{}init\symbol{}(\keya{}int\symbol{}\ \normal{}n\symbol{},\keya{}char\symbol{}\ **\normal{}s\symbol{})\ $\{$\ \normal{}number\symbol{}=\normal{}n\symbol{};\ \normal{}strings\symbol{}=\normal{}s\symbol{};\ $\}$ } 
     48\leftline{46:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
     49\leftline{47:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\keya{}int\symbol{}\ *\normal{}number\symbol{}); } 
     50\leftline{48:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getParam\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ \normal{}n\symbol{}=\normal{}1\symbol{}); } 
     51\leftline{49:\ \ \ } 
     52\leftline{50:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\keya{}int\symbol{}\ *\normal{}result\symbol{}); } 
     53\leftline{51:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\normal{}Real\symbol{}\ *\normal{}r\symbol{}); } 
     54\leftline{52:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{},\keya{}char\symbol{}\ *\normal{}result\symbol{}); } 
     55\leftline{53:\ \ \ } 
     56\leftline{54:\ \ \ $\}$; } 
     57\leftline{55:\ \ \ } 
    5858\leftline{56:\ \ \  } 
    59 \leftline{57:\ \ \ } 
    60 \keya{}\leftline{58:\ \ \ class\symbol{}\ \normal{}LexanC\symbol{}:\ \keya{}public\symbol{}\ \normal{}BaseC\symbol{} } 
    61 \leftline{59:\ \ \ $\{$ } 
    62 \keya{}\leftline{60:\ \ \ public\symbol{}: } 
    63 \normal{}\leftline{61:\ \ \ FILE\symbol{}\ *\normal{}file\symbol{}; } 
    64 \leftline{62:\ \ \ } 
    65 \keya{}\leftline{63:\ \ \ int\symbol{}\ \ \ \ \normal{}nsep\symbol{},\normal{}nkey\symbol{}[\normal{}2\symbol{}]; } 
    66 \keya{}\leftline{64:\ \ \ char\symbol{}\ \ \ *\normal{}separators\symbol{}; } 
    67 \keya{}\leftline{65:\ \ \ char\symbol{}\ \ \ **\normal{}key\symbol{}[\normal{}2\symbol{}]; } 
    68 \leftline{66:\ \ \ } 
    69 \normal{}\leftline{67:\ \ \ LexanC\symbol{}()$\{$\normal{}init\symbol{}("\normal{}default.lex\symbol{}");$\}$ } 
    70 \leftline{68:\ \ \ } 
    71 \keya{}\leftline{69:\ \ \ int\symbol{}\ \ \ \ \normal{}isSeparator\symbol{}(\keya{}int\symbol{}\ \normal{}c\symbol{})$\{$\ \keya{}return\symbol{}\ \normal{}strchr\symbol{}(\normal{}separators\symbol{},\normal{}c\symbol{})!=\normal{}NULL\symbol{};\ $\}$ } 
    72 \keya{}\leftline{70:\ \ \ int\symbol{}\ \ \ \ \normal{}isKey\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}); } 
    73 \leftline{71:\ \ \ } 
    74 \keya{}\leftline{72:\ \ \ int\symbol{}\ \ \ \ \normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}lexname\symbol{}); } 
    75 \keya{}\leftline{73:\ \ \ int\symbol{}\ \ \ \ \normal{}open\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}); } 
    76 \leftline{74:\ \ \ } 
    77 \keya{}\leftline{75:\ \ \ int\symbol{}\ \ \ \ \normal{}readWord\symbol{}(\normal{}FILE\symbol{}\ *\normal{}f\symbol{},\keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
    78 \leftline{76:\ \ \ } 
    79 \keya{}\leftline{77:\ \ \ int\symbol{}\ \ \ \ \normal{}read\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
    80 \keya{}\leftline{78:\ \ \ void\symbol{}\ \ \ \normal{}close\symbol{}()$\{$\normal{}fclose\symbol{}(\normal{}file\symbol{});$\}$ } 
    81 \leftline{79:\ \ \  } 
    82 \keya{}\leftline{80:\ \ \ virtual\symbol{}\ \ \ \ \ \ \ \ \normal{}ostream\symbol{}\&\ \keya{}operator\symbol{}>>(\normal{}ostream\symbol{}\ \&\normal{}s\symbol{})\ $\{$\ } 
    83 \leftline{81:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}<<\normal{}nsep\symbol{}<<"\\normal{}n\symbol{}"<<\normal{}nkey\symbol{}[\normal{}0\symbol{}]<<"\\normal{}n\symbol{}"<<\normal{}nkey\symbol{}[\normal{}1\symbol{}]<<"\\normal{}n\symbol{}"; } 
    84 \leftline{82:\ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}s\symbol{}; } 
    85 \leftline{83:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
    86 \leftline{84:\ \ \ \~{}\normal{}LexanC\symbol{}()$\{$\normal{}fclose\symbol{}(\normal{}file\symbol{});$\}$ } 
    87 \leftline{85:\ \ \ $\}$; } 
    88 \leftline{86:\ \ \ } 
    89 \keya{}\leftline{87:\ \ \ class\symbol{}\ \normal{}TexC\symbol{}:\ \keya{}public\symbol{}\ \normal{}BaseC\symbol{} } 
    90 \leftline{88:\ \ \ $\{$ } 
    91 \keya{}\leftline{89:\ \ \ public\symbol{}: } 
    92 \leftline{90:\ \ \ \ \ \normal{}FILE\symbol{}\ *\normal{}file\symbol{}; } 
    93 \leftline{91:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}numbers\symbol{}; } 
    94 \leftline{92:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}lnumber\symbol{}; } 
    95 \leftline{93:\ \ \ \ \ } 
    96 \leftline{94:\ \ \ \ \ \normal{}TexC\symbol{}()$\{$\normal{}numbers\symbol{}=\normal{}0\symbol{};\ \normal{}init\symbol{}("\normal{}output.tex\symbol{}");$\}$ } 
    97 \leftline{95:\ \ \ \ \ \normal{}TexC\symbol{}(\keya{}int\symbol{}\ \normal{}n\symbol{},\ \keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}s\symbol{})$\{$ } 
    98 \leftline{96:\ \ \ \ \ \ \ \normal{}lnumber\symbol{}=\normal{}0\symbol{}; } 
    99 \leftline{97:\ \ \ \ \ \ \ \normal{}numbers\symbol{}=\normal{}n\symbol{};\ \normal{}init\symbol{}(\normal{}s\symbol{});$\}$ } 
    100 \leftline{98:\ \ \ \ \ } 
    101 \leftline{99:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}); } 
    102 \leftline{100:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}write\symbol{}(\keya{}const\symbol{}\ \keya{}int\symbol{}\ \normal{}code\symbol{},\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
    103 \leftline{101:\ \ \ \ \ \~{}\normal{}TexC\symbol{}()\ $\{$\ \comment{}/*\ fprintf(file,"\n\\bye\n");\ */\symbol{}\ \normal{}fclose\symbol{}(\normal{}file\symbol{});$\}$ } 
    104 \leftline{102:\ \ \ } 
    105 \leftline{103:\ \ \ $\}$; } 
     59\keya{}\leftline{57:\ \ \ class\symbol{}\ \normal{}LexanC\symbol{}:\ \keya{}public\symbol{}\ \normal{}BaseC\symbol{} } 
     60\leftline{58:\ \ \ $\{$ } 
     61\keya{}\leftline{59:\ \ \ public\symbol{}: } 
     62\normal{}\leftline{60:\ \ \ FILE\symbol{}\ *\normal{}file\symbol{}; } 
     63\leftline{61:\ \ \ } 
     64\keya{}\leftline{62:\ \ \ int\symbol{}\ \ \ \ \normal{}nsep\symbol{},\normal{}nkey\symbol{}[\normal{}2\symbol{}]; } 
     65\keya{}\leftline{63:\ \ \ char\symbol{}\ \ \ *\normal{}separators\symbol{}; } 
     66\keya{}\leftline{64:\ \ \ char\symbol{}\ \ \ **\normal{}key\symbol{}[\normal{}2\symbol{}]; } 
     67\leftline{65:\ \ \ } 
     68\normal{}\leftline{66:\ \ \ LexanC\symbol{}()$\{$\normal{}init\symbol{}("\normal{}default.lex\symbol{}");$\}$ } 
     69\leftline{67:\ \ \ } 
     70\keya{}\leftline{68:\ \ \ int\symbol{}\ \ \ \ \normal{}isSeparator\symbol{}(\keya{}int\symbol{}\ \normal{}c\symbol{})$\{$\ \keya{}return\symbol{}\ \normal{}strchr\symbol{}(\normal{}separators\symbol{},\normal{}c\symbol{})!=\normal{}NULL\symbol{};\ $\}$ } 
     71\keya{}\leftline{69:\ \ \ int\symbol{}\ \ \ \ \normal{}isKey\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}); } 
     72\leftline{70:\ \ \ } 
     73\keya{}\leftline{71:\ \ \ int\symbol{}\ \ \ \ \normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}lexname\symbol{}); } 
     74\keya{}\leftline{72:\ \ \ int\symbol{}\ \ \ \ \normal{}open\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}); } 
     75\leftline{73:\ \ \ } 
     76\keya{}\leftline{74:\ \ \ int\symbol{}\ \ \ \ \normal{}readWord\symbol{}(\normal{}FILE\symbol{}\ *\normal{}f\symbol{},\keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
     77\leftline{75:\ \ \ } 
     78\keya{}\leftline{76:\ \ \ int\symbol{}\ \ \ \ \normal{}read\symbol{}(\keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
     79\keya{}\leftline{77:\ \ \ void\symbol{}\ \ \ \normal{}close\symbol{}()$\{$\normal{}fclose\symbol{}(\normal{}file\symbol{});$\}$ } 
     80\leftline{78:\ \ \ } 
     81\keya{}\leftline{79:\ \ \ virtual\symbol{}\ \ \ \ \ \ \ \ \normal{}ostream\symbol{}\&\ \keya{}operator\symbol{}>>(\normal{}ostream\symbol{}\ \&\normal{}s\symbol{})\ $\{$\  } 
     82\leftline{80:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}<<\normal{}nsep\symbol{}<<"\\normal{}n\symbol{}"<<\normal{}nkey\symbol{}[\normal{}0\symbol{}]<<"\\normal{}n\symbol{}"<<\normal{}nkey\symbol{}[\normal{}1\symbol{}]<<"\\normal{}n\symbol{}"; } 
     83\leftline{81:\ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}s\symbol{}; } 
     84\leftline{82:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
     85\leftline{83:\ \ \ \~{}\normal{}LexanC\symbol{}()$\{$\normal{}fclose\symbol{}(\normal{}file\symbol{});$\}$ } 
     86\leftline{84:\ \ \ $\}$; } 
     87\leftline{85:\ \ \ } 
     88\keya{}\leftline{86:\ \ \ class\symbol{}\ \normal{}TexC\symbol{}:\ \keya{}public\symbol{}\ \normal{}BaseC\symbol{} } 
     89\leftline{87:\ \ \ $\{$ } 
     90\keya{}\leftline{88:\ \ \ public\symbol{}: } 
     91\leftline{89:\ \ \ \ \ \normal{}FILE\symbol{}\ *\normal{}file\symbol{}; } 
     92\leftline{90:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}numbers\symbol{}; } 
     93\leftline{91:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}lnumber\symbol{}; } 
     94\leftline{92:\ \ \ \ \ } 
     95\leftline{93:\ \ \ \ \ \normal{}TexC\symbol{}()$\{$\normal{}numbers\symbol{}=\normal{}0\symbol{};\ \normal{}init\symbol{}("\normal{}output.tex\symbol{}");$\}$ } 
     96\leftline{94:\ \ \ \ \ \normal{}TexC\symbol{}(\keya{}int\symbol{}\ \normal{}n\symbol{},\ \keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}s\symbol{})$\{$ } 
     97\leftline{95:\ \ \ \ \ \ \ \normal{}lnumber\symbol{}=\normal{}0\symbol{}; } 
     98\leftline{96:\ \ \ \ \ \ \ \normal{}numbers\symbol{}=\normal{}n\symbol{};\ \normal{}init\symbol{}(\normal{}s\symbol{});$\}$ } 
     99\leftline{97:\ \ \ \ \ } 
     100\leftline{98:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}); } 
     101\leftline{99:\ \ \ \ \ \keya{}int\symbol{}\ \normal{}write\symbol{}(\keya{}const\symbol{}\ \keya{}int\symbol{}\ \normal{}code\symbol{},\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}string\symbol{}); } 
     102\leftline{100:\ \ \ \ \ \~{}\normal{}TexC\symbol{}()\ $\{$\ \symbol{}/\normal{}fclose\symbol{}(\normal{}file\symbol{});$\}$ } 
     103\leftline{101:\ \ \ } 
     104\leftline{102:\ \ \ $\}$; } 
     105\leftline{103:\ \ \ } 
    106106\leftline{104:\ \ \  } 
    107107\leftline{105:\ \ \  } 
    … …  
    111111\leftline{109:\ \ \  } 
    112112\leftline{110:\ \ \  } 
    113 \leftline{111:\ \ \ } 
    114 \keya{}\leftline{112:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
    115 \leftline{113:\ \ \ $\{$ } 
    116 \keya{}\leftline{114:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
    117 \leftline{115:\ \ \ \ } 
    118 \leftline{116:\ \ \ \ \keya{}for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
    119 \leftline{117:\ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strcmp\symbol{}(\normal{}s\symbol{},\normal{}strings\symbol{}[\normal{}i\symbol{}])==\normal{}0\symbol{})\ $\{$\ \normal{}found\symbol{}\ =\ \normal{}i\symbol{};\ \keya{}break\symbol{};$\}$ } 
    120 \leftline{118:\ \ \ } 
    121 \leftline{119:\ \ \ \ \keya{}return\symbol{}\ \normal{}found\symbol{}; } 
    122 \leftline{120:\ \ \ $\}$ } 
    123 \leftline{121:\ \ \ } 
    124 \keya{}\leftline{122:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ *\normal{}n\symbol{}) } 
    125 \leftline{123:\ \ \ $\{$ } 
    126 \keya{}\leftline{124:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
    127 \leftline{125:\ \ \ } 
    128 \keya{}\leftline{126:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
    129 \leftline{127:\ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strcmp\symbol{}(\normal{}s\symbol{},\normal{}strings\symbol{}[\normal{}i\symbol{}])==\normal{}0\symbol{}) } 
    130 \leftline{128:\ \ \ \ \ \ \ $\{$ } 
    131 \leftline{129:\ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}i\symbol{}+\normal{}1\symbol{}<\normal{}number\symbol{}) } 
    132 \leftline{130:\ \ \ \ \ \ \ \ \ \ $\{$ } 
    133 \leftline{131:\ \ \ \ \ \ \ \ \ \ \ \ *\normal{}n\symbol{}=\normal{}atoi\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}+\normal{}1\symbol{}]); } 
    134 \leftline{132:\ \ \ \ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
    135 \leftline{133:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    136 \leftline{134:\ \ \ \ \ \ \ \ \ \ $\}$ } 
    137 \leftline{135:\ \ \ \ \ \ \ $\}$ } 
    138 \leftline{136:\ \ \ } 
    139 \keya{}\leftline{137:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
    140 \leftline{138:\ \ \ $\}$ } 
     113\keya{}\leftline{111:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
     114\leftline{112:\ \ \ $\{$ } 
     115\keya{}\leftline{113:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
     116\leftline{114:\ \ \ \ } 
     117\leftline{115:\ \ \ \ \keya{}for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
     118\leftline{116:\ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strcmp\symbol{}(\normal{}s\symbol{},\normal{}strings\symbol{}[\normal{}i\symbol{}])==\normal{}0\symbol{})\ $\{$\ \normal{}found\symbol{}\ =\ \normal{}i\symbol{};\ \keya{}break\symbol{};$\}$ } 
     119\leftline{117:\ \ \ } 
     120\leftline{118:\ \ \ \ \keya{}return\symbol{}\ \normal{}found\symbol{}; } 
     121\leftline{119:\ \ \ $\}$ } 
     122\leftline{120:\ \ \ } 
     123\keya{}\leftline{121:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}isOption\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ *\normal{}n\symbol{}) } 
     124\leftline{122:\ \ \ $\{$ } 
     125\keya{}\leftline{123:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
     126\leftline{124:\ \ \ } 
     127\keya{}\leftline{125:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
     128\leftline{126:\ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strcmp\symbol{}(\normal{}s\symbol{},\normal{}strings\symbol{}[\normal{}i\symbol{}])==\normal{}0\symbol{}) } 
     129\leftline{127:\ \ \ \ \ \ \ $\{$ } 
     130\leftline{128:\ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}i\symbol{}+\normal{}1\symbol{}<\normal{}number\symbol{}) } 
     131\leftline{129:\ \ \ \ \ \ \ \ \ \ $\{$ } 
     132\leftline{130:\ \ \ \ \ \ \ \ \ \ \ \ *\normal{}n\symbol{}=\normal{}atoi\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}+\normal{}1\symbol{}]); } 
     133\leftline{131:\ \ \ \ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
     134\leftline{132:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     135\leftline{133:\ \ \ \ \ \ \ \ \ \ $\}$ } 
     136\leftline{134:\ \ \ \ \ \ \ $\}$ } 
     137\leftline{135:\ \ \ } 
     138\keya{}\leftline{136:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
     139\leftline{137:\ \ \ $\}$ } 
     140\leftline{138:\ \ \ } 
    141141\leftline{139:\ \ \  } 
    142 \leftline{140:\ \ \ } 
    143 \comment{}\leftline{141:\ \ \ //\ search\ for\ occurence\ of\ non-option } 
    144 \keya{}\leftline{142:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}getParam\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ \normal{}n\symbol{}) } 
    145 \leftline{143:\ \ \ $\{$ } 
    146 \keya{}\leftline{144:\ \ \ int\symbol{}\ \normal{}i\symbol{},\normal{}k\symbol{}=\normal{}1\symbol{},\normal{}found\symbol{}=\normal{}0\symbol{}; } 
    147 \leftline{145:\ \ \ } 
    148 \keya{}\leftline{146:\ \ \ for\symbol{}\ (\normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
    149 \leftline{147:\ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strings\symbol{}[\normal{}i\symbol{}][\normal{}0\symbol{}]!='-') } 
    150 \leftline{148:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}k\symbol{}==\normal{}n\symbol{})\ $\{$\ \normal{}found\symbol{}=\normal{}1\symbol{};\ \keya{}break\symbol{};$\}$ } 
    151 \leftline{149:\ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
    152 \leftline{150:\ \ \ \ \ \ \ \ \ \ \ \normal{}k\symbol{}++; } 
     142\symbol{}\leftline{140:\ \ \ /\ \normal{}OptionsC\symbol{}::\normal{}getParam\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ \normal{}n\symbol{}) } 
     143\leftline{141:\ \ \ $\{$ } 
     144\keya{}\leftline{142:\ \ \ int\symbol{}\ \normal{}i\symbol{},\normal{}k\symbol{}=\normal{}1\symbol{},\normal{}found\symbol{}=\normal{}0\symbol{}; } 
     145\leftline{143:\ \ \ } 
     146\keya{}\leftline{144:\ \ \ for\symbol{}\ (\normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
     147\leftline{145:\ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strings\symbol{}[\normal{}i\symbol{}][\normal{}0\symbol{}]!='-') } 
     148\leftline{146:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}k\symbol{}==\normal{}n\symbol{})\ $\{$\ \normal{}found\symbol{}=\normal{}1\symbol{};\ \keya{}break\symbol{};$\}$ } 
     149\leftline{147:\ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
     150\leftline{148:\ \ \ \ \ \ \ \ \ \ \ \normal{}k\symbol{}++; } 
     151\leftline{149:\ \ \ } 
     152\keya{}\leftline{150:\ \ \ if\symbol{}\ (\normal{}found\symbol{})\ \normal{}strcpy\symbol{}(\normal{}s\symbol{},\normal{}strings\symbol{}[\normal{}i\symbol{}]); } 
    153153\leftline{151:\ \ \  } 
    154 \keya{}\leftline{152:\ \ \ if\symbol{}\ (\normal{}found\symbol{})\ \normal{}strcpy\symbol{}(\normal{}s\symbol{},\normal{}strings\symbol{}[\normal{}i\symbol{}]); } 
    155 \leftline{153:\ \ \ } 
    156 \keya{}\leftline{154:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
    157 \leftline{155:\ \ \ $\}$ } 
     154\keya{}\leftline{152:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
     155\leftline{153:\ \ \ $\}$ } 
     156\leftline{154:\ \ \ } 
     157\leftline{155:\ \ \ } 
    158158\leftline{156:\ \ \  } 
    159 \leftline{157:\ \ \ } 
    160 \leftline{158:\ \ \ } 
    161 \keya{}\leftline{159:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ *\normal{}result\symbol{}) } 
    162 \leftline{160:\ \ \ $\{$ } 
    163 \keya{}\leftline{161:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
    164 \keya{}\leftline{162:\ \ \ char\symbol{}\ *\normal{}pp\symbol{}; } 
    165 \leftline{163:\ \ \ } 
    166 \keya{}\leftline{164:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
    167 \leftline{165:\ \ \ \ \ $\{$ } 
    168 \leftline{166:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}],\normal{}s\symbol{})) } 
    169 \leftline{167:\ \ \ \ \ \ \ $\{$ } 
    170 \leftline{168:\ \ \ \ \ \ \ \ \ \normal{}pp\symbol{}=\normal{}strings\symbol{}[\normal{}i\symbol{}]+\normal{}strlen\symbol{}(\normal{}s\symbol{}); } 
    171 \leftline{169:\ \ \ \ \ \ \ \ \ *\normal{}result\symbol{}=\normal{}atoi\symbol{}(\normal{}pp\symbol{}); } 
    172 \leftline{170:\ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
    173 \leftline{171:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    174 \leftline{172:\ \ \ \ \ \ \ $\}$ } 
    175 \leftline{173:\ \ \ \ \ $\}$ } 
    176 \keya{}\leftline{174:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
    177 \leftline{175:\ \ \ $\}$ } 
    178 \leftline{176:\ \ \ } 
    179 \keya{}\leftline{177:\ \ \ int\symbol{} } 
    180 \normal{}\leftline{178:\ \ \ OptionsC\symbol{}::\normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\normal{}Real\symbol{}\ *\normal{}result\symbol{}) } 
    181 \leftline{179:\ \ \ $\{$ } 
    182 \keya{}\leftline{180:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
    183 \keya{}\leftline{181:\ \ \ char\symbol{}\ *\normal{}pp\symbol{}; } 
    184 \leftline{182:\ \ \ } 
    185 \keya{}\leftline{183:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
    186 \leftline{184:\ \ \ \ \ $\{$ } 
    187 \leftline{185:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}],\normal{}s\symbol{})) } 
    188 \leftline{186:\ \ \ \ \ \ \ $\{$ } 
    189 \leftline{187:\ \ \ \ \ \ \ \ \ \normal{}pp\symbol{}=\normal{}strings\symbol{}[\normal{}i\symbol{}]+\normal{}strlen\symbol{}(\normal{}s\symbol{}); } 
    190 \leftline{188:\ \ \ \ \ \ \ \ \ *\normal{}result\symbol{}=\normal{}atof\symbol{}(\normal{}pp\symbol{}); } 
    191 \leftline{189:\ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
    192 \leftline{190:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    193 \leftline{191:\ \ \ \ \ \ \ $\}$ } 
    194 \leftline{192:\ \ \ \ \ $\}$ } 
    195 \keya{}\leftline{193:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
    196 \leftline{194:\ \ \ $\}$ } 
    197 \leftline{195:\ \ \ } 
    198 \keya{}\leftline{196:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}char\symbol{}\ *\normal{}result\symbol{}) } 
    199 \leftline{197:\ \ \ $\{$ } 
    200 \keya{}\leftline{198:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
    201 \keya{}\leftline{199:\ \ \ char\symbol{}\ *\normal{}pp\symbol{}; } 
    202 \leftline{200:\ \ \ } 
    203 \keya{}\leftline{201:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
    204 \leftline{202:\ \ \ \ \ $\{$ } 
    205 \leftline{203:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}],\normal{}s\symbol{})) } 
    206 \leftline{204:\ \ \ \ \ \ \ $\{$ } 
    207 \leftline{205:\ \ \ \ \ \ \ \ \ \normal{}pp\symbol{}=\normal{}strings\symbol{}[\normal{}i\symbol{}]+\normal{}strlen\symbol{}(\normal{}s\symbol{}); } 
    208 \leftline{206:\ \ \ \ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}result\symbol{},\normal{}pp\symbol{}); } 
    209 \leftline{207:\ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
    210 \leftline{208:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    211 \leftline{209:\ \ \ \ \ \ \ $\}$ } 
    212 \leftline{210:\ \ \ \ \ $\}$ } 
    213 \keya{}\leftline{211:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
    214 \leftline{212:\ \ \ $\}$ } 
    215 \leftline{213:\ \ \ } 
    216 \leftline{214:\ \ \ } 
    217 \keya{}\leftline{215:\ \ \ int\symbol{}\ \normal{}TexC\symbol{}::\normal{}write\symbol{}(\keya{}const\symbol{}\ \keya{}int\symbol{}\ \normal{}code\symbol{},\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}string\symbol{}) } 
    218 \leftline{216:\ \ \ $\{$ } 
    219 \keya{}\leftline{217:\ \ \ static\symbol{}\ \keya{}int\symbol{}\ \normal{}line\symbol{}=\normal{}1\symbol{}; } 
    220 \keya{}\leftline{218:\ \ \ static\symbol{}\ \keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
    221 \keya{}\leftline{219:\ \ \ const\symbol{}\ \keya{}char\symbol{}\ *\normal{}sp\symbol{}; } 
    222 \leftline{220:\ \ \ } 
    223 \keya{}\leftline{221:\ \ \ switch\symbol{}(\normal{}code\symbol{})\ $\{$ } 
    224 \leftline{222:\ \ \ \ \  } 
    225 \leftline{223:\ \ \ \ \keya{}case\symbol{}\ \normal{}KEYWORD1\symbol{}\ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}keya\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    226 \leftline{224:\ \ \ \ \keya{}case\symbol{}\ \normal{}KEYWORD2\symbol{}\ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}keyb\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    227 \leftline{225:\ \ \ \ \keya{}case\symbol{}\ \normal{}COMMENT\symbol{}\ \ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}comment\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    228 \leftline{226:\ \ \ \ \keya{}case\symbol{}\ \normal{}SYMBOL\symbol{}\ \ \ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}symbol\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    229 \leftline{227:\ \ \ \ \keya{}case\symbol{}\ \normal{}NORMAL\symbol{}\ \ \ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}normal\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    230 \leftline{228:\ \ \ $\}$ } 
    231 \leftline{229:\ \ \ \ } 
    232 \leftline{230:\ \ \ \ \keya{}for\symbol{}(\normal{}sp\symbol{}=\normal{}string\symbol{};*\normal{}sp\symbol{}!=\normal{}0\symbol{};\normal{}sp\symbol{}++,\normal{}i\symbol{}++) } 
    233 \leftline{231:\ \ \ \ \ \ $\{$ } 
    234 \leftline{232:\ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}line\symbol{})\ $\{$ } 
    235 \leftline{233:\ \ \ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}leftline\symbol{}$\{$");\ \normal{}line\symbol{}=\normal{}0\symbol{}; } 
    236 \leftline{234:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}string\symbol{},\ "\keya{}Algorithm\symbol{}")\ ==\ \normal{}string\symbol{}) } 
    237 \leftline{235:\ \ \ \ \ \ \ \ \ \ \ \normal{}lnumber\symbol{}\ =\ \normal{}0\symbol{}; } 
    238 \leftline{236:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}numbers\symbol{}\ \&\&\ \normal{}lnumber\symbol{}) } 
    239 \leftline{237:\ \ \ \ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}\%2d\symbol{}:\\\ \\\ \\\ ",\normal{}lnumber\symbol{}); } 
    240 \leftline{238:\ \ \ \ \ \ \ \ \ \ \normal{}lnumber\symbol{}++; } 
    241 \leftline{239:\ \ \ \ \ \ \ \ $\}$ } 
    242 \leftline{240:\ \ \ \ \ \ \ \ } 
    243 \leftline{241:\ \ \ \ \ \ \ \ \keya{}switch\symbol{}(*\normal{}sp\symbol{})\ $\{$ } 
    244 \leftline{242:\ \ \ \ \ \ \ \ \ \  } 
    245 \keyb{}\leftline{243:\ \ \ \#define\symbol{}\ \normal{}TAB_SIZE\symbol{}\ \ \ \ \ \ \ \normal{}8\symbol{} } 
    246 \leftline{244:\ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\\normal{}t\symbol{}'\ :\ \ \ \ \ \ \ } 
    247 \leftline{245:\ \ \ \ \ \ \ \ \ \ \comment{}//\ \ \ \ \ \ \ \ \ \ \ fprintf(file,"\\tab");\ break; } 
    248 \symbol{}\leftline{246:\ \ \ \ \ \ \ \ \ \ \keya{}int\symbol{}\ \normal{}n\symbol{},\normal{}j\symbol{}; } 
    249 \leftline{247:\ \ \ \ \ \ \ \ \ \ \normal{}n\symbol{}=((\normal{}i\symbol{}/\normal{}TAB_SIZE\symbol{})+\normal{}1\symbol{})*\normal{}TAB_SIZE\symbol{}-\normal{}i\symbol{}; } 
    250 \leftline{248:\ \ \ \ \ \ \ \ \ \ \keya{}for\symbol{}(\normal{}j\symbol{}=\normal{}0\symbol{};\normal{}j\symbol{}<\normal{}n\symbol{};\normal{}j\symbol{}++)\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\ "); } 
    251 \leftline{249:\ \ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    252 \leftline{250:\ \ \ \ \ \ \ \ \ \ } 
    253 \leftline{251:\ \ \ \ \ \ \ \ \ \ } 
    254 \leftline{252:\ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\\normal{}n\symbol{}':\ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\ $\}$\\normal{}n\symbol{}"); } 
    255 \leftline{253:\ \ \ \ \ \ \ \ \ \ \normal{}line\symbol{}=\normal{}1\symbol{};\ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
    256 \leftline{254:\ \ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    257 \leftline{255:\ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\ '\ :\ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\ "); } 
    258 \leftline{256:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    259 \leftline{257:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\&'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\&");\ \keya{}break\symbol{}; } 
    260 \leftline{258:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\normal{}\#\symbol{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}\#\symbol{}");\ \keya{}break\symbol{}; } 
    261 \leftline{259:\ \ \ \ \ \ \ \ \ \ \comment{}//\ \ \ \ \ \ case\ '_'\ :\ \ \ \ fprintf(file,"\\_$\{$$\}$");\ break; } 
    262 \symbol{}\leftline{260:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\normal{}_\symbol{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}_\symbol{}");\ \keya{}break\symbol{}; } 
    263 \leftline{261:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\^{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\^{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    264 \leftline{262:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\normal{}\%\symbol{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}\%\%\symbol{}");\ \keya{}break\symbol{}; } 
    265 \leftline{263:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '$\{$'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}$\symbol{}\\$\{$\normal{}$\symbol{}");\ \keya{}break\symbol{}; } 
    266 \leftline{264:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '$\}$'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}$\symbol{}\\$\}$\normal{}$\symbol{}");\ \keya{}break\symbol{}; } 
    267 \leftline{265:\ \ \ \ \ \ \ \ \ \ \comment{}//\ \ case\ '\\'\ :\ \ \ \ \ \ \ fprintf(file,"$\\backslash$");\ break; } 
    268 \symbol{}\leftline{266:\ \ \ \ \ \ \ \ \ \ \comment{}//\ \ case\ '$'\ :\ \ \ \ \ \ \ \ fprintf(file,"\\$");\ break; } 
    269 \symbol{}\leftline{267:\ \ \ \ \ \ \ \comment{}//\ \ case\ '<'\ :\ \ \ \ \ fprintf(file,"\\<");\ break; } 
    270 \symbol{}\leftline{268:\ \ \ \ \ \ \ \comment{}//\ \ case\ '>'\ :\ \ \ \ \ fprintf(file,"\\>");\ break; } 
    271 \symbol{}\leftline{269:\ \ \ } 
    272 \leftline{270:\ \ \ \ \ \keya{}case\symbol{}\ '\~{}'\ :\ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\~{}$\{$$\}$");\ \keya{}break\symbol{}; } 
    273 \leftline{271:\ \ \ \ \ \ \ \ \ } 
    274 \leftline{272:\ \ \ \ \ \ \ } 
    275 \leftline{273:\ \ \ \ \ \keya{}default\symbol{}\ \ :\ \ \ \normal{}fputc\symbol{}(*\normal{}sp\symbol{},\normal{}file\symbol{}); } 
    276 \leftline{274:\ \ \ \ \ $\}$ } 
    277 \leftline{275:\ \ \ $\}$ } 
    278 \keya{}\leftline{276:\ \ \ return\symbol{}\ \normal{}1\symbol{}; } 
    279 \leftline{277:\ \ \ $\}$ } 
    280 \leftline{278:\ \ \ } 
     159\keya{}\leftline{157:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}int\symbol{}\ *\normal{}result\symbol{}) } 
     160\leftline{158:\ \ \ $\{$ } 
     161\keya{}\leftline{159:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
     162\keya{}\leftline{160:\ \ \ char\symbol{}\ *\normal{}pp\symbol{}; } 
     163\leftline{161:\ \ \ } 
     164\keya{}\leftline{162:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
     165\leftline{163:\ \ \ \ \ $\{$ } 
     166\leftline{164:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}],\normal{}s\symbol{})) } 
     167\leftline{165:\ \ \ \ \ \ \ $\{$ } 
     168\leftline{166:\ \ \ \ \ \ \ \ \ \normal{}pp\symbol{}=\normal{}strings\symbol{}[\normal{}i\symbol{}]+\normal{}strlen\symbol{}(\normal{}s\symbol{}); } 
     169\leftline{167:\ \ \ \ \ \ \ \ \ *\normal{}result\symbol{}=\normal{}atoi\symbol{}(\normal{}pp\symbol{}); } 
     170\leftline{168:\ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
     171\leftline{169:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     172\leftline{170:\ \ \ \ \ \ \ $\}$ } 
     173\leftline{171:\ \ \ \ \ $\}$ } 
     174\keya{}\leftline{172:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
     175\leftline{173:\ \ \ $\}$ } 
     176\leftline{174:\ \ \ } 
     177\keya{}\leftline{175:\ \ \ int\symbol{} } 
     178\normal{}\leftline{176:\ \ \ OptionsC\symbol{}::\normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\normal{}Real\symbol{}\ *\normal{}result\symbol{}) } 
     179\leftline{177:\ \ \ $\{$ } 
     180\keya{}\leftline{178:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
     181\keya{}\leftline{179:\ \ \ char\symbol{}\ *\normal{}pp\symbol{}; } 
     182\leftline{180:\ \ \ } 
     183\keya{}\leftline{181:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
     184\leftline{182:\ \ \ \ \ $\{$ } 
     185\leftline{183:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}],\normal{}s\symbol{})) } 
     186\leftline{184:\ \ \ \ \ \ \ $\{$ } 
     187\leftline{185:\ \ \ \ \ \ \ \ \ \normal{}pp\symbol{}=\normal{}strings\symbol{}[\normal{}i\symbol{}]+\normal{}strlen\symbol{}(\normal{}s\symbol{}); } 
     188\leftline{186:\ \ \ \ \ \ \ \ \ *\normal{}result\symbol{}=\normal{}atof\symbol{}(\normal{}pp\symbol{}); } 
     189\leftline{187:\ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
     190\leftline{188:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     191\leftline{189:\ \ \ \ \ \ \ $\}$ } 
     192\leftline{190:\ \ \ \ \ $\}$ } 
     193\keya{}\leftline{191:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
     194\leftline{192:\ \ \ $\}$ } 
     195\leftline{193:\ \ \ } 
     196\keya{}\leftline{194:\ \ \ int\symbol{}\ \normal{}OptionsC\symbol{}::\normal{}getOptionValue\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{},\keya{}char\symbol{}\ *\normal{}result\symbol{}) } 
     197\leftline{195:\ \ \ $\{$ } 
     198\keya{}\leftline{196:\ \ \ int\symbol{}\ \normal{}found\symbol{}=\normal{}0\symbol{}; } 
     199\keya{}\leftline{197:\ \ \ char\symbol{}\ *\normal{}pp\symbol{}; } 
     200\leftline{198:\ \ \ } 
     201\keya{}\leftline{199:\ \ \ for\symbol{}\ (\keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}1\symbol{};\normal{}i\symbol{}<\normal{}number\symbol{};\normal{}i\symbol{}++) } 
     202\leftline{200:\ \ \ \ \ $\{$ } 
     203\leftline{201:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}strings\symbol{}[\normal{}i\symbol{}],\normal{}s\symbol{})) } 
     204\leftline{202:\ \ \ \ \ \ \ $\{$ } 
     205\leftline{203:\ \ \ \ \ \ \ \ \ \normal{}pp\symbol{}=\normal{}strings\symbol{}[\normal{}i\symbol{}]+\normal{}strlen\symbol{}(\normal{}s\symbol{}); } 
     206\leftline{204:\ \ \ \ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}result\symbol{},\normal{}pp\symbol{}); } 
     207\leftline{205:\ \ \ \ \ \ \ \ \ \normal{}found\symbol{}\ =\ \normal{}i\symbol{}; } 
     208\leftline{206:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     209\leftline{207:\ \ \ \ \ \ \ $\}$ } 
     210\leftline{208:\ \ \ \ \ $\}$ } 
     211\keya{}\leftline{209:\ \ \ return\symbol{}\ \normal{}found\symbol{}; } 
     212\leftline{210:\ \ \ $\}$ } 
     213\leftline{211:\ \ \ } 
     214\leftline{212:\ \ \ } 
     215\keya{}\leftline{213:\ \ \ int\symbol{}\ \normal{}TexC\symbol{}::\normal{}write\symbol{}(\keya{}const\symbol{}\ \keya{}int\symbol{}\ \normal{}code\symbol{},\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}string\symbol{}) } 
     216\leftline{214:\ \ \ $\{$ } 
     217\keya{}\leftline{215:\ \ \ static\symbol{}\ \keya{}int\symbol{}\ \normal{}line\symbol{}=\normal{}1\symbol{}; } 
     218\keya{}\leftline{216:\ \ \ static\symbol{}\ \keya{}int\symbol{}\ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
     219\keya{}\leftline{217:\ \ \ const\symbol{}\ \keya{}char\symbol{}\ *\normal{}sp\symbol{}; } 
     220\leftline{218:\ \ \ } 
     221\keya{}\leftline{219:\ \ \ switch\symbol{}(\normal{}code\symbol{})\ $\{$ } 
     222\leftline{220:\ \ \ \ \ } 
     223\leftline{221:\ \ \ \ \keya{}case\symbol{}\ \normal{}KEYWORD1\symbol{}\ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}keya\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     224\leftline{222:\ \ \ \ \keya{}case\symbol{}\ \normal{}KEYWORD2\symbol{}\ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}keyb\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     225\leftline{223:\ \ \ \ \keya{}case\symbol{}\ \normal{}COMMENT\symbol{}\ \ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}comment\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     226\leftline{224:\ \ \ \ \keya{}case\symbol{}\ \normal{}SYMBOL\symbol{}\ \ \ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}symbol\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     227\leftline{225:\ \ \ \ \keya{}case\symbol{}\ \normal{}NORMAL\symbol{}\ \ \ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}normal\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     228\leftline{226:\ \ \ $\}$ } 
     229\leftline{227:\ \ \ \ } 
     230\leftline{228:\ \ \ \ \keya{}for\symbol{}(\normal{}sp\symbol{}=\normal{}string\symbol{};*\normal{}sp\symbol{}!=\normal{}0\symbol{};\normal{}sp\symbol{}++,\normal{}i\symbol{}++) } 
     231\leftline{229:\ \ \ \ \ \ $\{$ } 
     232\leftline{230:\ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}line\symbol{})\ $\{$ } 
     233\leftline{231:\ \ \ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}leftline\symbol{}$\{$");\ \normal{}line\symbol{}=\normal{}0\symbol{}; } 
     234\leftline{232:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strstr\symbol{}(\normal{}string\symbol{},\ "\keya{}Algorithm\symbol{}")\ ==\ \normal{}string\symbol{}) } 
     235\leftline{233:\ \ \ \ \ \ \ \ \ \ \ \normal{}lnumber\symbol{}\ =\ \normal{}0\symbol{}; } 
     236\leftline{234:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}numbers\symbol{}\ \&\&\ \normal{}lnumber\symbol{}) } 
     237\leftline{235:\ \ \ \ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}\%2d\symbol{}:\\\ \\\ \\\ ",\normal{}lnumber\symbol{}); } 
     238\leftline{236:\ \ \ \ \ \ \ \ \ \ \normal{}lnumber\symbol{}++; } 
     239\leftline{237:\ \ \ \ \ \ \ \ $\}$ } 
     240\leftline{238:\ \ \ \ \ \ \ \ } 
     241\leftline{239:\ \ \ \ \ \ \ \ \keya{}switch\symbol{}(*\normal{}sp\symbol{})\ $\{$ } 
     242\leftline{240:\ \ \ \ \ \ \ \ \ \ } 
     243\keyb{}\leftline{241:\ \ \ \#define\symbol{}\ \normal{}TAB_SIZE\symbol{}\ \ \ \ \ \ \ \normal{}8\symbol{} } 
     244\leftline{242:\ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\\normal{}t\symbol{}'\ :\ \ \ \ \ \ \  } 
     245\leftline{243:\ \ \ \ \ \ \ \ \ \ \symbol{}/\normal{}IZE\symbol{})+\normal{}1\symbol{})*\normal{}TAB_SIZE\symbol{}-\normal{}i\symbol{}; } 
     246\leftline{244:\ \ \ \ \ \ \ \ \ \ \keya{}for\symbol{}(\normal{}j\symbol{}=\normal{}0\symbol{};\normal{}j\symbol{}<\normal{}n\symbol{};\normal{}j\symbol{}++)\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\ "); } 
     247\leftline{245:\ \ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     248\leftline{246:\ \ \ \ \ \ \ \ \ \ } 
     249\leftline{247:\ \ \ \ \ \ \ \ \ \ } 
     250\leftline{248:\ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\\normal{}n\symbol{}':\ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\ $\}$\\normal{}n\symbol{}"); } 
     251\leftline{249:\ \ \ \ \ \ \ \ \ \ \normal{}line\symbol{}=\normal{}1\symbol{};\ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
     252\leftline{250:\ \ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     253\leftline{251:\ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\ '\ :\ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\ "); } 
     254\leftline{252:\ \ \ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     255\leftline{253:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\&'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\&");\ \keya{}break\symbol{}; } 
     256\leftline{254:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\normal{}\#\symbol{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}\#\symbol{}");\ \keya{}break\symbol{}; } 
     257\leftline{255:\ \ \ \ \ \ \ \ \ \ \symbol{}/'\normal{}_\symbol{}'\ :\ \ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}_\symbol{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     258\leftline{256:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\normal{}_\symbol{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}_\symbol{}");\ \keya{}break\symbol{}; } 
     259\leftline{257:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\^{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\^{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     260\leftline{258:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '\normal{}\%\symbol{}'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}\%\%\symbol{}");\ \keya{}break\symbol{}; } 
     261\leftline{259:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '$\{$'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}$\symbol{}\\$\{$\normal{}$\symbol{}");\ \keya{}break\symbol{}; } 
     262\leftline{260:\ \ \ \ \ \ \ \ \ \keya{}case\symbol{}\ '$\}$'\ :\ \ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}$\symbol{}\\$\}$\normal{}$\symbol{}");\ \keya{}break\symbol{}; } 
     263\leftline{261:\ \ \ \ \ \ \ \ \ \ \symbol{}/\normal{}e\symbol{}\ '\\'\ :\ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\normal{}$\symbol{}\\\normal{}backslash$\symbol{}");\ \keya{}break\symbol{}; } 
     264\leftline{262:\ \ \ \ \ \ \ \ \ \ \symbol{}/\ \ \keya{}case\symbol{}\ '\normal{}$\symbol{}'\ :\ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}$\symbol{}");\ \keya{}break\symbol{}; } 
     265\leftline{263:\ \ \ \ \ \ \ \symbol{}/\ \ \keya{}case\symbol{}\ '<'\ :\ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\<");\ \keya{}break\symbol{}; } 
     266\leftline{264:\ \ \ \ \ \ \ \symbol{}/\ \ \keya{}case\symbol{}\ '>'\ :\ \ \ \ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\>");\ \keya{}break\symbol{}; } 
     267\leftline{265:\ \ \ } 
     268\leftline{266:\ \ \ \ \ \keya{}case\symbol{}\ '\~{}'\ :\ \ \ \normal{}fprintf\symbol{}(\normal{}file\symbol{},"\\\~{}$\{$$\}$");\ \keya{}break\symbol{}; } 
     269\leftline{267:\ \ \ \ \ \ \ \ \ } 
     270\leftline{268:\ \ \ \ \ \ \ } 
     271\leftline{269:\ \ \ \ \ \keya{}default\symbol{}\ \ :\ \ \ \normal{}fputc\symbol{}(*\normal{}sp\symbol{},\normal{}file\symbol{}); } 
     272\leftline{270:\ \ \ \ \ $\}$ } 
     273\leftline{271:\ \ \ $\}$ } 
     274\keya{}\leftline{272:\ \ \ return\symbol{}\ \normal{}1\symbol{}; } 
     275\leftline{273:\ \ \ $\}$ } 
     276\leftline{274:\ \ \ } 
     277\leftline{275:\ \ \ } 
     278\keya{}\leftline{276:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}isKey\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
     279\leftline{277:\ \ \ $\{$ } 
     280\keya{}\leftline{278:\ \ \ int\symbol{}\ \normal{}i\symbol{},\normal{}j\symbol{}; } 
    281281\leftline{279:\ \ \  } 
    282 \keya{}\leftline{280:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}isKey\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
    283 \leftline{281:\ \ \ $\{$ } 
    284 \keya{}\leftline{282:\ \ \ int\symbol{}\ \normal{}i\symbol{},\normal{}j\symbol{}; } 
     282\keya{}\leftline{280:\ \ \ for\symbol{}\ (\normal{}i\symbol{}=\normal{}0\symbol{};\normal{}i\symbol{}<\normal{}2\symbol{};\normal{}i\symbol{}++) } 
     283\leftline{281:\ \ \ \ \keya{}for\symbol{}(\normal{}j\symbol{}=\normal{}0\symbol{};\normal{}j\symbol{}<\normal{}nkey\symbol{}[\normal{}i\symbol{}];\normal{}j\symbol{}++) } 
     284\leftline{282:\ \ \ \ \ \keya{}if\symbol{}(\normal{}strcmp\symbol{}(\normal{}s\symbol{},\normal{}key\symbol{}[\normal{}i\symbol{}][\normal{}j\symbol{}])==\normal{}0\symbol{})\ \keya{}return\symbol{}\ \normal{}i\symbol{}+\normal{}1\symbol{}; } 
    285285\leftline{283:\ \ \  } 
    286 \keya{}\leftline{284:\ \ \ for\symbol{}\ (\normal{}i\symbol{}=\normal{}0\symbol{};\normal{}i\symbol{}<\normal{}2\symbol{};\normal{}i\symbol{}++) } 
    287 \leftline{285:\ \ \ \ \keya{}for\symbol{}(\normal{}j\symbol{}=\normal{}0\symbol{};\normal{}j\symbol{}<\normal{}nkey\symbol{}[\normal{}i\symbol{}];\normal{}j\symbol{}++) } 
    288 \leftline{286:\ \ \ \ \ \keya{}if\symbol{}(\normal{}strcmp\symbol{}(\normal{}s\symbol{},\normal{}key\symbol{}[\normal{}i\symbol{}][\normal{}j\symbol{}])==\normal{}0\symbol{})\ \keya{}return\symbol{}\ \normal{}i\symbol{}+\normal{}1\symbol{}; } 
     286\keya{}\leftline{284:\ \ \ return\symbol{}\ \normal{}0\symbol{}; } 
     287\leftline{285:\ \ \ $\}$ } 
     288\leftline{286:\ \ \ } 
    289289\leftline{287:\ \ \  } 
    290 \keya{}\leftline{288:\ \ \ return\symbol{}\ \normal{}0\symbol{}; } 
    291 \leftline{289:\ \ \ $\}$ } 
    292 \leftline{290:\ \ \ } 
    293 \leftline{291:\ \ \ } 
    294 \keya{}\leftline{292:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}readWord\symbol{}(\normal{}FILE\symbol{}\ *\normal{}f\symbol{},\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
    295 \leftline{293:\ \ \ $\{$ } 
    296 \keya{}\leftline{294:\ \ \ int\symbol{}\ \ \ \ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
    297 \keya{}\leftline{295:\ \ \ int\symbol{}\ \ \ \ \normal{}c\symbol{}; } 
    298 \leftline{296:\ \ \ } 
    299 \keya{}\leftline{297:\ \ \ do\symbol{} } 
    300 \leftline{298:\ \ \ $\{$ } 
    301 \leftline{299:\ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}f\symbol{}); } 
    302 \leftline{300:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}return\symbol{}\ \normal{}0\symbol{}; } 
    303 \leftline{301:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (!\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ \keya{}break\symbol{}; } 
    304 \leftline{302:\ \ \ $\}$\ \keya{}while\symbol{}(\normal{}1\symbol{}); } 
    305 \leftline{303:\ \ \ } 
    306 \leftline{304:\ \ \ } 
    307 \keya{}\leftline{305:\ \ \ do\symbol{} } 
    308 \leftline{306:\ \ \ $\{$ } 
    309 \leftline{307:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
    310 \leftline{308:\ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}f\symbol{}); } 
    311 \leftline{309:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
    312 \leftline{310:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ \keya{}break\symbol{}; } 
    313 \leftline{311:\ \ \ $\}$\ \keya{}while\symbol{}(\normal{}1\symbol{}); } 
     290\keya{}\leftline{288:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}readWord\symbol{}(\normal{}FILE\symbol{}\ *\normal{}f\symbol{},\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
     291\leftline{289:\ \ \ $\{$ } 
     292\keya{}\leftline{290:\ \ \ int\symbol{}\ \ \ \ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
     293\keya{}\leftline{291:\ \ \ int\symbol{}\ \ \ \ \normal{}c\symbol{}; } 
     294\leftline{292:\ \ \ } 
     295\keya{}\leftline{293:\ \ \ do\symbol{} } 
     296\leftline{294:\ \ \ $\{$ } 
     297\leftline{295:\ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}f\symbol{}); } 
     298\leftline{296:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}return\symbol{}\ \normal{}0\symbol{}; } 
     299\leftline{297:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (!\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ \keya{}break\symbol{}; } 
     300\leftline{298:\ \ \ $\}$\ \keya{}while\symbol{}(\normal{}1\symbol{}); } 
     301\leftline{299:\ \ \ } 
     302\leftline{300:\ \ \ } 
     303\keya{}\leftline{301:\ \ \ do\symbol{} } 
     304\leftline{302:\ \ \ $\{$ } 
     305\leftline{303:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     306\leftline{304:\ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}f\symbol{}); } 
     307\leftline{305:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
     308\leftline{306:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ \keya{}break\symbol{}; } 
     309\leftline{307:\ \ \ $\}$\ \keya{}while\symbol{}(\normal{}1\symbol{}); } 
     310\leftline{308:\ \ \ } 
     311\normal{}\leftline{309:\ \ \ s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
     312\keya{}\leftline{310:\ \ \ return\symbol{}\ \normal{}1\symbol{}; } 
     313\leftline{311:\ \ \ $\}$ } 
    314314\leftline{312:\ \ \  } 
    315 \normal{}\leftline{313:\ \ \ s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
    316 \keya{}\leftline{314:\ \ \ return\symbol{}\ \normal{}1\symbol{}; } 
    317 \leftline{315:\ \ \ $\}$ } 
    318 \leftline{316:\ \ \ } 
     315\leftline{313:\ \ \ } 
     316\keya{}\leftline{314:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}lexname\symbol{}) } 
     317\leftline{315:\ \ \ $\{$ } 
     318\normal{}\leftline{316:\ \ \ FILE\symbol{}\ *\normal{}f\symbol{}; } 
    319319\leftline{317:\ \ \  } 
    320 \keya{}\leftline{318:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}lexname\symbol{}) } 
    321 \leftline{319:\ \ \ $\{$ } 
    322 \normal{}\leftline{320:\ \ \ FILE\symbol{}\ *\normal{}f\symbol{}; } 
    323 \leftline{321:\ \ \ } 
    324 \keya{}\leftline{322:\ \ \ if\symbol{}\ ((\normal{}f\symbol{}=\normal{}fopen\symbol{}(\normal{}lexname\symbol{},"\normal{}rt\symbol{}"))==\normal{}NULL\symbol{})\ \keya{}return\symbol{}\ \normal{}0\symbol{}; } 
    325 \leftline{323:\ \ \ } 
    326 \leftline{324:\ \ \ } 
    327 \keyb{}\leftline{325:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}NKEY\symbol{}\ \ \normal{}512\symbol{} } 
     320\keya{}\leftline{318:\ \ \ if\symbol{}\ ((\normal{}f\symbol{}=\normal{}fopen\symbol{}(\normal{}lexname\symbol{},"\normal{}rt\symbol{}"))==\normal{}NULL\symbol{})\ \keya{}return\symbol{}\ \normal{}0\symbol{}; } 
     321\leftline{319:\ \ \ } 
     322\leftline{320:\ \ \ } 
     323\keyb{}\leftline{321:\ \ \ \#define\symbol{}\ \ \ \ \ \ \ \ \normal{}NKEY\symbol{}\ \ \normal{}512\symbol{} } 
     324\leftline{322:\ \ \ } 
     325\normal{}\leftline{323:\ \ \ separators\symbol{}\ =\ \keya{}new\symbol{}\ \keya{}char\symbol{}[\normal{}256\symbol{}]; } 
     326\normal{}\leftline{324:\ \ \ key\symbol{}[\normal{}0\symbol{}]\ \ \ =\ \keya{}new\symbol{}\ \keya{}char\symbol{}*[\normal{}NKEY\symbol{}]; } 
     327\normal{}\leftline{325:\ \ \ key\symbol{}[\normal{}1\symbol{}]\ \ \ =\ \keya{}new\symbol{}\ \keya{}char\symbol{}*[\normal{}NKEY\symbol{}]; } 
    328328\leftline{326:\ \ \  } 
    329 \normal{}\leftline{327:\ \ \ separators\symbol{}\ =\ \keya{}new\symbol{}\ \keya{}char\symbol{}[\normal{}256\symbol{}]; } 
    330 \normal{}\leftline{328:\ \ \ key\symbol{}[\normal{}0\symbol{}]\ \ \ =\ \keya{}new\symbol{}\ \keya{}char\symbol{}*[\normal{}NKEY\symbol{}]; } 
    331 \normal{}\leftline{329:\ \ \ key\symbol{}[\normal{}1\symbol{}]\ \ \ =\ \keya{}new\symbol{}\ \keya{}char\symbol{}*[\normal{}NKEY\symbol{}]; } 
    332 \leftline{330:\ \ \ } 
    333 \normal{}\leftline{331:\ \ \ nsep\symbol{}=\normal{}0\symbol{}; } 
    334 \normal{}\leftline{332:\ \ \ separators\symbol{}[\normal{}nsep\symbol{}++]='\ '; } 
    335 \normal{}\leftline{333:\ \ \ separators\symbol{}[\normal{}nsep\symbol{}++]='\\normal{}n\symbol{}'; } 
    336 \normal{}\leftline{334:\ \ \ separators\symbol{}[\normal{}nsep\symbol{}++]='\\normal{}t\symbol{}'; } 
    337 \leftline{335:\ \ \ } 
    338 \normal{}\leftline{336:\ \ \ fgets\symbol{}(\normal{}separators\symbol{}+\normal{}nsep\symbol{},\normal{}100\symbol{},\normal{}f\symbol{}); } 
    339 \normal{}\leftline{337:\ \ \ nsep\symbol{}=\normal{}strlen\symbol{}(\normal{}separators\symbol{}); } 
    340 \leftline{338:\ \ \ } 
    341 \keya{}\leftline{339:\ \ \ int\symbol{}\ \normal{}i\symbol{}=\normal{}0\symbol{},\normal{}type\symbol{}=\normal{}0\symbol{}; } 
    342 \keya{}\leftline{340:\ \ \ char\symbol{}\ \normal{}s\symbol{}[\normal{}64\symbol{}]; } 
    343 \leftline{341:\ \ \ } 
    344 \keya{}\leftline{342:\ \ \ while\symbol{}(\normal{}readWord\symbol{}(\normal{}f\symbol{},\normal{}s\symbol{})) } 
    345 \leftline{343:\ \ \ \ \ \ \ \ \ \ $\{$ } 
    346 \leftline{344:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strcmp\symbol{}(\normal{}s\symbol{},"\normal{}KEYWORDS2\symbol{}")==\normal{}0\symbol{})\  } 
    347 \leftline{345:\ \ \ \ \ \ \ \ \ \ $\{$ } 
    348 \leftline{346:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}printf\symbol{}("\normal{}KEYWORDS2\symbol{}\\normal{}n\symbol{}"); } 
    349 \leftline{347:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}nkey\symbol{}[\normal{}type\symbol{}]=\normal{}i\symbol{};\ } 
    350 \leftline{348:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}type\symbol{}++;\ } 
    351 \leftline{349:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}i\symbol{}=\normal{}0\symbol{};\ \  } 
    352 \leftline{350:\ \ \ \ \ \ \ \ \ \ $\}$ } 
    353 \leftline{351:\ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
    354 \leftline{352:\ \ \ \ \ \ \ \ \ \ $\{$ } 
    355 \leftline{353:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}printf\symbol{}("\normal{}\%s\symbol{}\\normal{}n\symbol{}",\normal{}s\symbol{});\ \ \ \ } 
    356 \leftline{354:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}key\symbol{}[\normal{}type\symbol{}][\normal{}i\symbol{}]=\keya{}new\symbol{}\ \keya{}char\symbol{}[\normal{}strlen\symbol{}(\normal{}s\symbol{})+\normal{}1\symbol{}]; } 
    357 \leftline{355:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}key\symbol{}[\normal{}type\symbol{}][\normal{}i\symbol{}],\normal{}s\symbol{}); } 
    358 \leftline{356:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}i\symbol{}++; } 
    359 \leftline{357:\ \ \ \ \ \ \ \ \ \ $\}$ } 
    360 \leftline{358:\ \ \ \ \ \ \ \ \ \ $\}$ } 
     329\normal{}\leftline{327:\ \ \ nsep\symbol{}=\normal{}0\symbol{}; } 
     330\normal{}\leftline{328:\ \ \ separators\symbol{}[\normal{}nsep\symbol{}++]='\ '; } 
     331\normal{}\leftline{329:\ \ \ separators\symbol{}[\normal{}nsep\symbol{}++]='\\normal{}n\symbol{}'; } 
     332\normal{}\leftline{330:\ \ \ separators\symbol{}[\normal{}nsep\symbol{}++]='\\normal{}t\symbol{}'; } 
     333\leftline{331:\ \ \ } 
     334\normal{}\leftline{332:\ \ \ fgets\symbol{}(\normal{}separators\symbol{}+\normal{}nsep\symbol{},\normal{}100\symbol{},\normal{}f\symbol{}); } 
     335\normal{}\leftline{333:\ \ \ nsep\symbol{}=\normal{}strlen\symbol{}(\normal{}separators\symbol{}); } 
     336\leftline{334:\ \ \ } 
     337\keya{}\leftline{335:\ \ \ int\symbol{}\ \normal{}i\symbol{}=\normal{}0\symbol{},\normal{}type\symbol{}=\normal{}0\symbol{}; } 
     338\keya{}\leftline{336:\ \ \ char\symbol{}\ \normal{}s\symbol{}[\normal{}64\symbol{}]; } 
     339\leftline{337:\ \ \ } 
     340\keya{}\leftline{338:\ \ \ while\symbol{}(\normal{}readWord\symbol{}(\normal{}f\symbol{},\normal{}s\symbol{})) } 
     341\leftline{339:\ \ \ \ \ \ \ \ \ \ $\{$ } 
     342\leftline{340:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}strcmp\symbol{}(\normal{}s\symbol{},"\normal{}KEYWORDS2\symbol{}")==\normal{}0\symbol{})\ } 
     343\leftline{341:\ \ \ \ \ \ \ \ \ \ $\{$ } 
     344\leftline{342:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}printf\symbol{}("\normal{}KEYWORDS2\symbol{}\\normal{}n\symbol{}"); } 
     345\leftline{343:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}nkey\symbol{}[\normal{}type\symbol{}]=\normal{}i\symbol{};\ } 
     346\leftline{344:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}type\symbol{}++;\  } 
     347\leftline{345:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}i\symbol{}=\normal{}0\symbol{};\ \ } 
     348\leftline{346:\ \ \ \ \ \ \ \ \ \ $\}$ } 
     349\leftline{347:\ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
     350\leftline{348:\ \ \ \ \ \ \ \ \ \ $\{$ } 
     351\leftline{349:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}printf\symbol{}("\normal{}\%s\symbol{}\\normal{}n\symbol{}",\normal{}s\symbol{});\ \ \ \  } 
     352\leftline{350:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}key\symbol{}[\normal{}type\symbol{}][\normal{}i\symbol{}]=\keya{}new\symbol{}\ \keya{}char\symbol{}[\normal{}strlen\symbol{}(\normal{}s\symbol{})+\normal{}1\symbol{}]; } 
     353\leftline{351:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}key\symbol{}[\normal{}type\symbol{}][\normal{}i\symbol{}],\normal{}s\symbol{}); } 
     354\leftline{352:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}i\symbol{}++; } 
     355\leftline{353:\ \ \ \ \ \ \ \ \ \ $\}$ } 
     356\leftline{354:\ \ \ \ \ \ \ \ \ \ $\}$ } 
     357\leftline{355:\ \ \ } 
     358\normal{}\leftline{356:\ \ \ nkey\symbol{}[\normal{}type\symbol{}]=\normal{}i\symbol{};\ } 
     359\leftline{357:\ \ \ } 
     360\normal{}\leftline{358:\ \ \ fclose\symbol{}(\normal{}f\symbol{}); } 
    361361\leftline{359:\ \ \  } 
    362 \normal{}\leftline{360:\ \ \ nkey\symbol{}[\normal{}type\symbol{}]=\normal{}i\symbol{};\  } 
    363 \leftline{361:\ \ \ } 
    364 \normal{}\leftline{362:\ \ \ fclose\symbol{}(\normal{}f\symbol{}); } 
     362\leftline{360:\ \ \ \  } 
     363\keya{}\leftline{361:\ \ \ return\symbol{}\ \normal{}1\symbol{}; } 
     364\leftline{362:\ \ \ $\}$ } 
    365365\leftline{363:\ \ \  } 
    366 \leftline{364:\ \ \ \ } 
    367 \keya{}\leftline{365:\ \ \ return\symbol{}\ \normal{}1\symbol{}; } 
    368 \leftline{366:\ \ \ $\}$ } 
    369 \leftline{367:\ \ \ } 
    370 \keya{}\leftline{368:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}open\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}) } 
    371 \leftline{369:\ \ \ $\{$ } 
    372 \normal{}\leftline{370:\ \ \ file\symbol{}=\normal{}fopen\symbol{}(\normal{}filename\symbol{},"\normal{}rt\symbol{}"); } 
    373 \keya{}\leftline{371:\ \ \ return\symbol{}\ \normal{}file\symbol{}!=\normal{}NULL\symbol{}; } 
    374 \leftline{372:\ \ \ $\}$ } 
    375 \leftline{373:\ \ \ } 
    376 \keya{}\leftline{374:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}read\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
    377 \leftline{375:\ \ \ $\{$ } 
    378 \keya{}\leftline{376:\ \ \ int\symbol{}\ \ \ \ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
    379 \keya{}\leftline{377:\ \ \ int\symbol{}\ \ \ \ \normal{}type\symbol{}=\normal{}EOF\symbol{}; } 
    380 \keya{}\leftline{378:\ \ \ int\symbol{}\ \ \ \ \normal{}wasSlash\symbol{}=\normal{}0\symbol{}; } 
    381 \keya{}\leftline{379:\ \ \ static\symbol{}\ \keya{}int\symbol{}\ \ \ \ \ \normal{}c\symbol{}=-\normal{}2\symbol{}; } 
     366\keya{}\leftline{364:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}open\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}) } 
     367\leftline{365:\ \ \ $\{$ } 
     368\normal{}\leftline{366:\ \ \ file\symbol{}=\normal{}fopen\symbol{}(\normal{}filename\symbol{},"\normal{}rt\symbol{}"); } 
     369\keya{}\leftline{367:\ \ \ return\symbol{}\ \normal{}file\symbol{}!=\normal{}NULL\symbol{}; } 
     370\leftline{368:\ \ \ $\}$ } 
     371\leftline{369:\ \ \ } 
     372\keya{}\leftline{370:\ \ \ int\symbol{}\ \ \ \ \normal{}LexanC\symbol{}::\normal{}read\symbol{}(\keya{}char\symbol{}\ *\normal{}s\symbol{}) } 
     373\leftline{371:\ \ \ $\{$ } 
     374\keya{}\leftline{372:\ \ \ int\symbol{}\ \ \ \ \normal{}i\symbol{}=\normal{}0\symbol{}; } 
     375\keya{}\leftline{373:\ \ \ int\symbol{}\ \ \ \ \normal{}type\symbol{}=\normal{}EOF\symbol{}; } 
     376\keya{}\leftline{374:\ \ \ int\symbol{}\ \ \ \ \normal{}wasSlash\symbol{}=\normal{}0\symbol{}; } 
     377\keya{}\leftline{375:\ \ \ static\symbol{}\ \keya{}int\symbol{}\ \ \ \ \ \normal{}c\symbol{}=-\normal{}2\symbol{}; } 
     378\leftline{376:\ \ \ } 
     379\keya{}\leftline{377:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==-\normal{}2\symbol{})\ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     380\leftline{378:\ \ \ } 
     381\keya{}\leftline{379:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}return\symbol{}\ \normal{}c\symbol{}; } 
    382382\leftline{380:\ \ \  } 
    383 \keya{}\leftline{381:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==-\normal{}2\symbol{})\ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    384 \leftline{382:\ \ \ } 
    385 \keya{}\leftline{383:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}return\symbol{}\ \normal{}c\symbol{}; } 
    386 \leftline{384:\ \ \ } 
    387 \keya{}\leftline{385:\ \ \ do\symbol{}$\{$ } 
    388 \keya{}\leftline{386:\ \ \ if\symbol{}\ (\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ $\{$ } 
    389 \leftline{387:\ \ \ \ \ \ \ \ \ \ \normal{}type\symbol{}=\normal{}SYMBOL\symbol{}; } 
    390 \leftline{388:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
    391 \leftline{389:\ \ \ } 
    392 \leftline{390:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}wasSlash\symbol{})\ $\{$ } 
    393 \leftline{391:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}comment_1b\symbol{})\ \ \comment{}//\ line\ comment } 
    394 \symbol{}\leftline{392:\ \ \ \ \ \ \ \ \ \ \ \ \ \ $\{$\ } 
    395 \leftline{393:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}i\symbol{}>\normal{}2\symbol{})\ $\{$\ \normal{}c\symbol{}=\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}];\ \normal{}fseek\symbol{}(\normal{}file\symbol{},-\normal{}1\symbol{},\normal{}SEEK_CUR\symbol{});\ \normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]=\normal{}0\symbol{};\ } 
    396 \leftline{394:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}type\symbol{};\ $\}$ } 
    397 \leftline{395:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}fgets\symbol{}(\normal{}s\symbol{}+\normal{}i\symbol{},\normal{}S_LEN\symbol{},\normal{}file\symbol{});\ } 
    398 \leftline{396:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    399 \leftline{397:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}COMMENT\symbol{}; } 
    400 \leftline{398:\ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
    401 \leftline{399:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
    402 \leftline{400:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}comment_2b\symbol{})\ \ \comment{}//\ line\ comment } 
    403 \symbol{}\leftline{401:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\{$\  } 
    404 \leftline{402:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}i\symbol{}>\normal{}2\symbol{})\ $\{$\ \normal{}c\symbol{}=\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}];\ \normal{}fseek\symbol{}(\normal{}file\symbol{},-\normal{}1\symbol{},\normal{}SEEK_CUR\symbol{});\ \normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]=\normal{}0\symbol{};\ } 
    405 \leftline{403:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}type\symbol{};\ $\}$ } 
    406 \leftline{404:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    407 \leftline{405:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}!=\normal{}EOF\symbol{}) } 
    408 \leftline{406:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\{$ } 
    409 \leftline{407:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
    410 \leftline{408:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}int\symbol{}\ \normal{}count\symbol{}=\normal{}1\symbol{}; } 
    411 \leftline{409:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}do\symbol{}$\{$ } 
    412 \leftline{410:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    413 \leftline{411:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
    414 \leftline{412:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
    415 \leftline{413:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]==\normal{}comment_1e\symbol{}\ \&\&\ \normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}1\symbol{}]==\normal{}comment_2e\symbol{})\ \normal{}count\symbol{}--; } 
    416 \leftline{414:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]==\normal{}comment_1b\symbol{}\ \&\&\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}1\symbol{}]==\normal{}comment_2b\symbol{})\ \normal{}count\symbol{}++; } 
    417 \leftline{415:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$\ \keya{}while\symbol{}(\normal{}count\symbol{}!=\normal{}0\symbol{}); } 
    418 \leftline{416:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
    419 \leftline{417:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
    420 \leftline{418:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    421 \leftline{419:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}COMMENT\symbol{}; } 
    422 \leftline{420:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
    423 \leftline{421:\ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
    424 \leftline{422:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
    425 \leftline{423:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}wasSlash\symbol{}=(\normal{}c\symbol{}==\normal{}comment_1b\symbol{});\ } 
    426 \leftline{424:\ \ \ $\}$\ \keya{}else\symbol{} } 
    427 \leftline{425:\ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}=='')\ $\{$ } 
    428 \leftline{426:\ \ \ \ \ \ \ if\ (i>=1)\ $\{$ } 
    429 \leftline{427:\ \ \ \ \ \ \ \ \ s[i]=0; } 
    430 \leftline{428:\ \ \ \ \ \ \ \ \ return\ type; } 
    431 \leftline{429:\ \ \ \ \ \ \ $\}$ } 
    432 \leftline{430:\ \ \ \ \ \ \ c=fgetc(file); } 
    433 \leftline{431:\ \ \ \ \ \ \ while(c!='\symbol{}')\ $\{$ } 
    434 \leftline{432:\ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
    435 \leftline{433:\ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
    436 \leftline{434:\ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     383\keya{}\leftline{381:\ \ \ do\symbol{}$\{$ } 
     384\keya{}\leftline{382:\ \ \ if\symbol{}\ (\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ $\{$ } 
     385\leftline{383:\ \ \ \ \ \ \ \ \ \ \normal{}type\symbol{}=\normal{}SYMBOL\symbol{}; } 
     386\leftline{384:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     387\leftline{385:\ \ \ } 
     388\leftline{386:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}wasSlash\symbol{})\ $\{$ } 
     389\leftline{387:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}comment_1b\symbol{})\ \ \symbol{}/\ (\normal{}c\symbol{}==-\normal{}2\symbol{})\ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     390\leftline{388:\ \ \ } 
     391\keya{}\leftline{389:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}return\symbol{}\ \normal{}c\symbol{}; } 
     392\leftline{390:\ \ \ } 
     393\keya{}\leftline{391:\ \ \ do\symbol{}$\{$ } 
     394\keya{}\leftline{392:\ \ \ if\symbol{}\ (\normal{}isSeparator\symbol{}(\normal{}c\symbol{}))\ $\{$ } 
     395\leftline{393:\ \ \ \ \ \ \ \ \ \ \normal{}type\symbol{}=\normal{}SYMBOL\symbol{}; } 
     396\leftline{394:\ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     397\leftline{395:\ \ \ } 
     398\leftline{396:\ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}wasSlash\symbol{})\ $\{$ } 
     399\leftline{397:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}comment_1b\symbol{})\ \ \symbol{}/\normal{}ment\symbol{} } 
     400\leftline{398:\ \ \ \ \ \ \ \ \ \ \ \ \ \ $\{$\ } 
     401\leftline{399:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}i\symbol{}>\normal{}2\symbol{})\ $\{$\ \normal{}c\symbol{}=\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}];\ \normal{}fseek\symbol{}(\normal{}file\symbol{},-\normal{}1\symbol{},\normal{}SEEK_CUR\symbol{});\ \normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]=\normal{}0\symbol{};\ } 
     402\leftline{400:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}type\symbol{};\ $\}$ } 
     403\leftline{401:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}fgets\symbol{}(\normal{}s\symbol{}+\normal{}i\symbol{},\normal{}S_LEN\symbol{},\normal{}file\symbol{});\  } 
     404\leftline{402:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     405\leftline{403:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}COMMENT\symbol{}; } 
     406\leftline{404:\ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
     407\leftline{405:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
     408\leftline{406:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}comment_2b\symbol{})\ \ \symbol{}/\normal{}mment\symbol{} } 
     409\leftline{407:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\{$\ } 
     410\leftline{408:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}i\symbol{}>\normal{}2\symbol{})\ $\{$\ \normal{}c\symbol{}=\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}];\ \normal{}fseek\symbol{}(\normal{}file\symbol{},-\normal{}1\symbol{},\normal{}SEEK_CUR\symbol{});\ \normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]=\normal{}0\symbol{};\ } 
     411\leftline{409:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}type\symbol{};\ $\}$ } 
     412\leftline{410:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     413\leftline{411:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}!=\normal{}EOF\symbol{}) } 
     414\leftline{412:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\{$ } 
     415\leftline{413:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     416\leftline{414:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}int\symbol{}\ \normal{}count\symbol{}=\normal{}1\symbol{}; } 
     417\leftline{415:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}do\symbol{}$\{$ } 
     418\leftline{416:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     419\leftline{417:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}(\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
     420\leftline{418:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     421\leftline{419:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]==\normal{}comment_1e\symbol{}\ \&\&\ \normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}1\symbol{}]==\normal{}comment_2e\symbol{})\ \normal{}count\symbol{}--; } 
     422\leftline{420:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}2\symbol{}]==\normal{}comment_1b\symbol{}\ \&\&\normal{}s\symbol{}[\normal{}i\symbol{}-\normal{}1\symbol{}]==\normal{}comment_2b\symbol{})\ \normal{}count\symbol{}++; } 
     423\leftline{421:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$\ \keya{}while\symbol{}(\normal{}count\symbol{}!=\normal{}0\symbol{}); } 
     424\leftline{422:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
     425\leftline{423:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
     426\leftline{424:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     427\leftline{425:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}COMMENT\symbol{}; } 
     428\leftline{426:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
     429\leftline{427:\ \ \ \ \ \ \ \ \ \ \ \ \ \ $\}$ } 
     430\leftline{428:\ \ \ \ \ \ \ \ \ \ \ \ \keya{}else\symbol{} } 
     431\leftline{429:\ \ \ \ \ \ \ \ \ \ \ \ \ \ \normal{}wasSlash\symbol{}=(\normal{}c\symbol{}==\normal{}comment_1b\symbol{});\ } 
     432\leftline{430:\ \ \ $\}$\ \keya{}else\symbol{} } 
     433\leftline{431:\ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}=='')\ $\{$ } 
     434\leftline{432:\ \ \ \ \ \ \ if\ (i>=1)\ $\{$ } 
     435\leftline{433:\ \ \ \ \ \ \ \ \ s[i]=0; } 
     436\leftline{434:\ \ \ \ \ \ \ \ \ return\ type; } 
    437437\leftline{435:\ \ \ \ \ \ \ $\}$ } 
    438 \leftline{436:\ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
    439 \leftline{437:\ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    440 \leftline{438:\ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}WORD\symbol{}; } 
    441 \leftline{439:\ \ \ \ \ $\}$\ \keya{}else\symbol{}\ $\{$ } 
    442 \leftline{440:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}type\symbol{}==\normal{}EOF\symbol{})\ \normal{}type\symbol{}=\normal{}WORD\symbol{}; } 
    443 \leftline{441:\ \ \ \ \ \ \ \keya{}break\symbol{}; } 
    444 \leftline{442:\ \ \ \ \ $\}$ } 
    445 \leftline{443:\ \ \ $\}$\ \keya{}while\symbol{}((\normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}))!=\normal{}EOF\symbol{}); } 
    446 \leftline{444:\ \ \ \ } 
    447 \keya{}\leftline{445:\ \ \ if\symbol{}\ (\normal{}c\symbol{}!=\normal{}EOF\symbol{}) } 
    448 \keya{}\leftline{446:\ \ \ if\symbol{}\ (\normal{}type\symbol{}==\normal{}WORD\symbol{}) } 
    449 \leftline{447:\ \ \ $\{$ } 
    450 \keya{}\leftline{448:\ \ \ do\symbol{} } 
    451 \leftline{449:\ \ \ $\{$ } 
    452 \normal{}\leftline{450:\ \ \ s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
    453 \normal{}\leftline{451:\ \ \ c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
    454 \keya{}\leftline{452:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
    455 \leftline{453:\ \ \ $\}$\ \keya{}while\symbol{}(!\normal{}isSeparator\symbol{}(\normal{}c\symbol{})); } 
    456 \leftline{454:\ \ \ } 
    457 \normal{}\leftline{455:\ \ \ s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
    458 \keya{}\leftline{456:\ \ \ int\symbol{}\ \normal{}k\symbol{}=\normal{}isKey\symbol{}(\normal{}s\symbol{}); } 
    459 \keya{}\leftline{457:\ \ \ if\symbol{}\ (\normal{}k\symbol{}==\normal{}0\symbol{})\ \keya{}return\symbol{}\ \normal{}NORMAL\symbol{}; } 
    460 \keya{}\leftline{458:\ \ \ return\symbol{}\ \normal{}k\symbol{}; } 
    461 \leftline{459:\ \ \ $\}$ } 
     438\leftline{436:\ \ \ \ \ \ \ c=fgetc(file); } 
     439\leftline{437:\ \ \ \ \ \ \ while(c!='\symbol{}')\ $\{$ } 
     440\leftline{438:\ \ \ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
     441\leftline{439:\ \ \ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     442\leftline{440:\ \ \ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     443\leftline{441:\ \ \ \ \ \ \ $\}$ } 
     444\leftline{442:\ \ \ \ \ \ \ \normal{}s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
     445\leftline{443:\ \ \ \ \ \ \ \normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     446\leftline{444:\ \ \ \ \ \ \ \keya{}return\symbol{}\ \normal{}WORD\symbol{}; } 
     447\leftline{445:\ \ \ \ \ $\}$\ \keya{}else\symbol{}\ $\{$ } 
     448\leftline{446:\ \ \ \ \ \ \ \keya{}if\symbol{}\ (\normal{}type\symbol{}==\normal{}EOF\symbol{})\ \normal{}type\symbol{}=\normal{}WORD\symbol{}; } 
     449\leftline{447:\ \ \ \ \ \ \ \keya{}break\symbol{}; } 
     450\leftline{448:\ \ \ \ \ $\}$ } 
     451\leftline{449:\ \ \ $\}$\ \keya{}while\symbol{}((\normal{}c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}))!=\normal{}EOF\symbol{}); } 
     452\leftline{450:\ \ \ \ } 
     453\keya{}\leftline{451:\ \ \ if\symbol{}\ (\normal{}c\symbol{}!=\normal{}EOF\symbol{}) } 
     454\keya{}\leftline{452:\ \ \ if\symbol{}\ (\normal{}type\symbol{}==\normal{}WORD\symbol{}) } 
     455\leftline{453:\ \ \ $\{$ } 
     456\keya{}\leftline{454:\ \ \ do\symbol{} } 
     457\leftline{455:\ \ \ $\{$ } 
     458\normal{}\leftline{456:\ \ \ s\symbol{}[\normal{}i\symbol{}++]=\normal{}c\symbol{}; } 
     459\normal{}\leftline{457:\ \ \ c\symbol{}=\normal{}fgetc\symbol{}(\normal{}file\symbol{}); } 
     460\keya{}\leftline{458:\ \ \ if\symbol{}\ (\normal{}c\symbol{}==\normal{}EOF\symbol{})\ \keya{}break\symbol{}; } 
     461\leftline{459:\ \ \ $\}$\ \keya{}while\symbol{}(!\normal{}isSeparator\symbol{}(\normal{}c\symbol{})); } 
    462462\leftline{460:\ \ \  } 
    463463\normal{}\leftline{461:\ \ \ s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
    464 \keya{}\leftline{462:\ \ \ return\symbol{}\ \normal{}type\symbol{}; } 
    465 \leftline{463:\ \ \  } 
    466 \leftline{464:\ \ \ $\}$ } 
    467 \leftline{465:\ \ \  } 
    468 \keya{}\leftline{466:\ \ \ int\symbol{}\ \normal{}TexC\symbol{}::\normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}) } 
    469 \leftline{467:\ \ \ $\{$ } 
    470 \normal{}\leftline{468:\ \ \ file\symbol{}=\normal{}fopen\symbol{}(\normal{}filename\symbol{},"\normal{}wt\symbol{}"); } 
    471 \normal{}\leftline{469:\ \ \ fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}input\symbol{}\ \normal{}default.mac\symbol{}\\normal{}n\symbol{}"); } 
    472 \keya{}\leftline{470:\ \ \ return\symbol{}\ \normal{}file\symbol{}!=\normal{}NULL\symbol{}; } 
    473 \leftline{471:\ \ \ $\}$ } 
    474 \leftline{472:\ \ \  } 
    475 \leftline{473:\ \ \  } 
    476 \keya{}\leftline{474:\ \ \ void\symbol{}\ \normal{}Help\symbol{}() } 
    477 \leftline{475:\ \ \ $\{$ } 
    478 \leftline{476:\ \ \ \ \ \normal{}printf\symbol{}("\normal{}Syntax\symbol{}\ :\ \normal{}c2tex\symbol{}\ \normal{}in_file\symbol{}\ [\normal{}out_file\symbol{}]\ [-\keya{}pascal\symbol{}]\ [-\normal{}numbers\symbol{}]\\normal{}n\symbol{}\\normal{}n\symbol{}"); } 
    479 \leftline{477:\ \ \ \ \ \normal{}printf\symbol{}("\normal{}Default\symbol{}\ \normal{}out_file\symbol{}\ \normal{}is\symbol{}\ \normal{}output.tex.\symbol{}\\normal{}n\symbol{}"); } 
    480 \leftline{478:\ \ \ \ \ \normal{}printf\symbol{}("-\keya{}pascal\symbol{}\ \ \ \ \ \normal{}use\symbol{}\ \keya{}pascal\symbol{}\ \normal{}comments\symbol{}\ (*\ *)\normal{}.\symbol{}\\normal{}n\symbol{}"); } 
    481 \leftline{479:\ \ \ \ \ \normal{}printf\symbol{}("-\normal{}numbers\symbol{}\ \ \ \ \normal{}print\symbol{}\ \normal{}line\symbol{}\ \normal{}numbers.\symbol{}\\normal{}n\symbol{}"); } 
    482 \leftline{480:\ \ \ \ \ \normal{}exit\symbol{}(\normal{}1\symbol{}); } 
    483 \leftline{481:\ \ \ $\}$ } 
    484 \leftline{482:\ \ \  } 
    485 \keya{}\leftline{483:\ \ \ int\symbol{}\ \normal{}main\symbol{}(\keya{}int\symbol{}\ \normal{}argc\symbol{},\ \keya{}char\symbol{}\ **\normal{}argv\symbol{}) } 
    486 \leftline{484:\ \ \ $\{$ } 
    487 \keya{}\leftline{485:\ \ \ char\symbol{}\ \ \ \normal{}s\symbol{}[\normal{}16384\symbol{}];\ \comment{}//\ docasny\ buffer } 
    488 \symbol{}\leftline{486:\ \ \  } 
    489 \normal{}\leftline{487:\ \ \ OptionsC\symbol{}\ \normal{}options\symbol{}(\normal{}argc\symbol{},\normal{}argv\symbol{}); } 
    490 \leftline{488:\ \ \ \  } 
    491 \leftline{489:\ \ \ \ \keya{}if\symbol{}\ (\normal{}argc\symbol{}<\normal{}2\symbol{})\ \normal{}Help\symbol{}(); } 
    492 \leftline{490:\ \ \ \ \keya{}if\symbol{}\ (\normal{}argc\symbol{}>=\normal{}3\symbol{}\ \&\&\ (*\normal{}argv\symbol{}[\normal{}2\symbol{}]!='-')\ ) } 
    493 \leftline{491:\ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}s\symbol{},\normal{}argv\symbol{}[\normal{}2\symbol{}]); } 
    494 \leftline{492:\ \ \ \ \keya{}else\symbol{} } 
    495 \leftline{493:\ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}s\symbol{},"\normal{}output.tex\symbol{}"); } 
    496 \leftline{494:\ \ \ \  } 
    497 \leftline{495:\ \ \ \ \keya{}if\symbol{}\ (\normal{}options.isOption\symbol{}("-\keya{}pascal\symbol{}"))\ $\{$ } 
    498 \leftline{496:\ \ \ \ \ \ \ \keya{}pascal\symbol{}=\normal{}1\symbol{}; } 
    499 \leftline{497:\ \ \ \ \ \ \ \normal{}comment_1b\symbol{}='('; } 
    500 \leftline{498:\ \ \ \ \ \ \ \normal{}comment_2b\symbol{}='*'; } 
    501 \leftline{499:\ \ \ \ \ \ \ \normal{}comment_1e\symbol{}='*'; } 
    502 \leftline{500:\ \ \ \ \ \ \ \normal{}comment_2e\symbol{}=')'; } 
    503 \leftline{501:\ \ \ \ $\}$ } 
    504 \leftline{502:\ \ \  } 
    505 \leftline{503:\ \ \ \ \keya{}int\symbol{}\ \normal{}numbers\symbol{}; } 
    506 \leftline{504:\ \ \ \  } 
    507 \leftline{505:\ \ \ \ \normal{}numbers\symbol{}\ =\ \normal{}options.isOption\symbol{}("-\normal{}numbers\symbol{}"); } 
    508 \leftline{506:\ \ \ \  } 
    509 \leftline{507:\ \ \ \  } 
    510 \leftline{508:\ \ \ \ \ \normal{}LexanC\symbol{}\ \ \ \ \ \ \ \normal{}lexan\symbol{}; } 
    511 \leftline{509:\ \ \ \ \ \normal{}TexC\symbol{}\ \normal{}tex\symbol{}(\normal{}numbers\symbol{},\ \normal{}s\symbol{}); } 
    512 \leftline{510:\ \ \ \ \ \normal{}lexan.open\symbol{}(\normal{}argv\symbol{}[\normal{}1\symbol{}]); } 
    513 \leftline{511:\ \ \  } 
    514 \leftline{512:\ \ \ \ \ \keya{}int\symbol{}\ \ \normal{}t\symbol{}; } 
    515 \leftline{513:\ \ \ \ \ \normal{}cout\symbol{}<<\normal{}tex.numbers\symbol{}; } 
    516 \leftline{514:\ \ \  } 
    517 \leftline{515:\ \ \ \ \  } 
    518 \leftline{516:\ \ \ \ \ \keya{}while\symbol{}((\normal{}t\symbol{}=\normal{}lexan.read\symbol{}(\normal{}s\symbol{}))!=\normal{}EOF\symbol{}) } 
    519 \leftline{517:\ \ \ \ \ \ \ $\{$ } 
    520 \leftline{518:\ \ \ \ \ \ \ \ \ \comment{}//printf("Type\ :\ \%d\ -\ \%s\n",t,s); } 
    521 \symbol{}\leftline{519:\ \ \ \ \ \ \ \ \ \normal{}printf\symbol{}("\normal{}\%s\symbol{}",\normal{}s\symbol{}); } 
    522 \leftline{520:\ \ \ \ \ \ \ \ \ \normal{}tex.write\symbol{}(\normal{}t\symbol{},\normal{}s\symbol{}); } 
    523 \leftline{521:\ \ \ \ \ \ \ \ \ \comment{}//getchar(); } 
    524 \symbol{}\leftline{522:\ \ \ \ \ \ \ $\}$ } 
    525 \leftline{523:\ \ \  } 
    526 \keya{}\leftline{524:\ \ \ return\symbol{}\ \normal{}0\symbol{}; } 
    527 \leftline{525:\ \ \ $\}$ } 
    528 \leftline{526:\ \ \  } 
    529 \leftline{527:\ \ \  } 
    530 \leftline{528:\ \ \  } 
    531 \leftline{529:\ \ \  } 
    532 \leftline{530:\ \ \  } 
     464\keya{}\leftline{462:\ \ \ int\symbol{}\ \normal{}k\symbol{}=\normal{}isKey\symbol{}(\normal{}s\symbol{}); } 
     465\keya{}\leftline{463:\ \ \ if\symbol{}\ (\normal{}k\symbol{}==\normal{}0\symbol{})\ \keya{}return\symbol{}\ \normal{}NORMAL\symbol{}; } 
     466\keya{}\leftline{464:\ \ \ return\symbol{}\ \normal{}k\symbol{}; } 
     467\leftline{465:\ \ \ $\}$ } 
     468\leftline{466:\ \ \  } 
     469\normal{}\leftline{467:\ \ \ s\symbol{}[\normal{}i\symbol{}]=\normal{}0\symbol{}; } 
     470\keya{}\leftline{468:\ \ \ return\symbol{}\ \normal{}type\symbol{}; } 
     471\leftline{469:\ \ \  } 
     472\leftline{470:\ \ \ $\}$ } 
     473\leftline{471:\ \ \  } 
     474\keya{}\leftline{472:\ \ \ int\symbol{}\ \normal{}TexC\symbol{}::\normal{}init\symbol{}(\keya{}const\symbol{}\ \keya{}char\symbol{}\ *\normal{}filename\symbol{}) } 
     475\leftline{473:\ \ \ $\{$ } 
     476\normal{}\leftline{474:\ \ \ file\symbol{}=\normal{}fopen\symbol{}(\normal{}filename\symbol{},"\normal{}wt\symbol{}"); } 
     477\normal{}\leftline{475:\ \ \ fprintf\symbol{}(\normal{}file\symbol{},"\\\normal{}input\symbol{}\ \normal{}default.mac\symbol{}\\normal{}n\symbol{}"); } 
     478\keya{}\leftline{476:\ \ \ return\symbol{}\ \normal{}file\symbol{}!=\normal{}NULL\symbol{}; } 
     479\leftline{477:\ \ \ $\}$ } 
     480\leftline{478:\ \ \  } 
     481\leftline{479:\ \ \  } 
     482\keya{}\leftline{480:\ \ \ void\symbol{}\ \normal{}Help\symbol{}() } 
     483\leftline{481:\ \ \ $\{$ } 
     484\leftline{482:\ \ \ \ \ \normal{}printf\symbol{}("\normal{}Syntax\symbol{}\ :\ \normal{}c2tex\symbol{}\ \normal{}in_file\symbol{}\ [\normal{}out_file\symbol{}]\ [-\normal{}usePascal\symbol{}]\ [-\normal{}numbers\symbol{}]\\normal{}n\symbol{}\\normal{}n\symbol{}"); } 
     485\leftline{483:\ \ \ \ \ \normal{}printf\symbol{}("\normal{}Default\symbol{}\ \normal{}out_file\symbol{}\ \normal{}is\symbol{}\ \normal{}output.tex.\symbol{}\\normal{}n\symbol{}"); } 
     486\leftline{484:\ \ \ \ \ \normal{}printf\symbol{}("-\normal{}usePascal\symbol{}\ \ \ \ \ \normal{}use\symbol{}\ \normal{}usePascal\symbol{}\ \normal{}comments\symbol{}\ (*\ *)\normal{}.\symbol{}\\normal{}n\symbol{}"); } 
     487\leftline{485:\ \ \ \ \ \normal{}printf\symbol{}("-\normal{}numbers\symbol{}\ \ \ \ \normal{}print\symbol{}\ \normal{}line\symbol{}\ \normal{}numbers.\symbol{}\\normal{}n\symbol{}"); } 
     488\leftline{486:\ \ \ \ \ \normal{}exit\symbol{}(\normal{}1\symbol{}); } 
     489\leftline{487:\ \ \ $\}$ } 
     490\leftline{488:\ \ \  } 
     491\keya{}\leftline{489:\ \ \ int\symbol{}\ \normal{}main\symbol{}(\keya{}int\symbol{}\ \normal{}argc\symbol{},\ \keya{}char\symbol{}\ **\normal{}argv\symbol{}) } 
     492\leftline{490:\ \ \ $\{$ } 
     493\keya{}\leftline{491:\ \ \ char\symbol{}\ \ \ \normal{}s\symbol{}[\normal{}16384\symbol{}];\ \symbol{}/\normal{}main\symbol{}(\keya{}int\symbol{}\ \normal{}argc\symbol{},\ \keya{}char\symbol{}\ **\normal{}argv\symbol{}) } 
     494\leftline{492:\ \ \ $\{$ } 
     495\keya{}\leftline{493:\ \ \ char\symbol{}\ \ \ \normal{}s\symbol{}[\normal{}16384\symbol{}];\ \symbol{}/\normal{}docasny\symbol{}\ \normal{}buffer\symbol{} } 
     496\leftline{494:\ \ \  } 
     497\normal{}\leftline{495:\ \ \ OptionsC\symbol{}\ \normal{}options\symbol{}(\normal{}argc\symbol{},\normal{}argv\symbol{}); } 
     498\leftline{496:\ \ \ \  } 
     499\leftline{497:\ \ \ \ \keya{}if\symbol{}\ (\normal{}argc\symbol{}<\normal{}2\symbol{})\ \normal{}Help\symbol{}(); } 
     500\leftline{498:\ \ \ \ \keya{}if\symbol{}\ (\normal{}argc\symbol{}>=\normal{}3\symbol{}\ \&\&\ (*\normal{}argv\symbol{}[\normal{}2\symbol{}]!='-')\ ) } 
     501\leftline{499:\ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}s\symbol{},\normal{}argv\symbol{}[\normal{}2\symbol{}]); } 
     502\leftline{500:\ \ \ \ \keya{}else\symbol{} } 
     503\leftline{501:\ \ \ \ \ \ \normal{}strcpy\symbol{}(\normal{}s\symbol{},"\normal{}output.tex\symbol{}"); } 
     504\leftline{502:\ \ \ \  } 
     505\leftline{503:\ \ \ \ \keya{}if\symbol{}\ (\normal{}options.isOption\symbol{}("-\normal{}usePascal\symbol{}"))\ $\{$ } 
     506\leftline{504:\ \ \ \ \ \ \ \normal{}usePascal\symbol{}=\normal{}1\symbol{}; } 
     507\leftline{505:\ \ \ \ \ \ \ \normal{}comment_1b\symbol{}='('; } 
     508\leftline{506:\ \ \ \ \ \ \ \normal{}comment_2b\symbol{}='*'; } 
     509\leftline{507:\ \ \ \ \ \ \ \normal{}comment_1e\symbol{}='*'; } 
     510\leftline{508:\ \ \ \ \ \ \ \normal{}comment_2e\symbol{}=')'; } 
     511\leftline{509:\ \ \ \ $\}$ } 
     512\leftline{510:\ \ \  } 
     513\leftline{511:\ \ \ \ \keya{}int\symbol{}\ \normal{}numbers\symbol{}; } 
     514\leftline{512:\ \ \ \  } 
     515\leftline{513:\ \ \ \ \normal{}numbers\symbol{}\ =\ \normal{}options.isOption\symbol{}("-\normal{}numbers\symbol{}"); } 
     516\leftline{514:\ \ \ \  } 
     517\leftline{515:\ \ \ \  } 
     518\leftline{516:\ \ \ \ \ \normal{}LexanC\symbol{}\ \ \ \ \ \ \ \normal{}lexan\symbol{}; } 
     519\leftline{517:\ \ \ \ \ \normal{}TexC\symbol{}\ \normal{}tex\symbol{}(\normal{}numbers\symbol{},\ \normal{}s\symbol{}); } 
     520\leftline{518:\ \ \ \ \ \normal{}lexan.open\symbol{}(\normal{}argv\symbol{}[\normal{}1\symbol{}]); } 
     521\leftline{519:\ \ \  } 
     522\leftline{520:\ \ \ \ \ \keya{}int\symbol{}\ \ \normal{}t\symbol{}; } 
     523\leftline{521:\ \ \ \ \ \normal{}cout\symbol{}<<\normal{}tex.numbers\symbol{}; } 
     524\leftline{522:\ \ \  } 
     525\leftline{523:\ \ \ \ \  } 
     526\leftline{524:\ \ \ \ \ \keya{}while\symbol{}((\normal{}t\symbol{}=\normal{}lexan.read\symbol{}(\normal{}s\symbol{}))!=\normal{}EOF\symbol{}) } 
     527\leftline{525:\ \ \ \ \ \ \ $\{$ } 
     528\leftline{526:\ \ \ \ \ \ \ \ \ \symbol{}/\ \ \ \ \ \ \normal{}printf\symbol{}("\normal{}\%s\symbol{}",\normal{}s\symbol{}); } 
     529\leftline{527:\ \ \ \ \ \ \ \ \ \normal{}tex.write\symbol{}(\normal{}t\symbol{},\normal{}s\symbol{}); } 
     530\leftline{528:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     531\leftline{529:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     532\leftline{530:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     533\leftline{531:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     534\leftline{532:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     535\leftline{533:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     536\leftline{534:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     537\leftline{535:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     538\leftline{536:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     539\leftline{537:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     540\leftline{538:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     541\leftline{539:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     542\leftline{540:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     543\leftline{541:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     544\leftline{542:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     545\leftline{543:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     546\leftline{544:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     547\leftline{545:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     548\leftline{546:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     549\leftline{547:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     550\leftline{548:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
     551\leftline{549:\ \ \ \ \ \ \ \ \symbol{}\ \normal{}s\symbol{}); } 
     552\leftline{550:\ \ \ \ \ \ \ \ \ \symbol{}/); } 
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  • trunk/VUT/doc/SciReport/code/makefile

    r243 r255  
    55TEX = ${PAS:.pas=.tex} ${CPP:.cpp=.tex} 
    66 
    7 C2TEX = ./c2tex 
     7C2TEX = ./c2tex.exe 
    88 
    99 

  • trunk/VUT/doc/SciReport/introduction.tex

    r249 r255  
    1 \chapter{Introduction} 
     1\chapter{Overview of visibility problems and algorithms}%\chapter 
     2 
     3\label{chap:overview} 
     4\label{chap:classes} 
     5 
     6 This chapter provides a taxonomy of visibility problems encountered 
     7in computer graphics based on the {\em problem domain} and the {\em 
     8type of the answer}. The taxonomy helps to understand the nature of a 
     9particular visibility problem and provides a tool for grouping 
     10problems of similar complexity independently of their target 
     11application. We discuss typical visibility problems encountered in 
     12computer graphics and identify their relation to the proposed 
     13taxonomy. A visibility problem can be solved by means of various 
     14visibility algorithms. We classify visibility algorithms according to 
     15several important criteria and discuss important concepts in the 
     16design of a visibility algorithm. The taxonomy and the discussion of 
     17the algorithm design sums up ideas and concepts that are independent 
     18of any specific algorithm. This can help algorithm designers to 
     19transfer the existing algorithms to solve visibility problems in other 
     20application areas. 
     21 
     22 
     23 
     24%% summarize the state of the art 
     25%% visibility algorithms and their relation to the proposed taxonomy of 
     26%% visibility problems. The second part of the survey should serve as a 
     27%% catalogue of visibility algorithms that is indexed by the proposed 
     28%% taxonomy of visibility problems. 
     29 
     30 
     31 
     32\subsection{Problem domain} 
     33\label{sec:prob_domain} 
     34 
     35 Computer graphics deals with visibility problems in the context of 
     362D, \m25d, or 3D scenes. The actual problem domain is given by 
     37restricting the set of rays for which visibility should be 
     38determined. 
     39 
     40Below we list common problem domains used and the corresponding domain 
     41restrictions: 
     42 
     43\begin{enumerate} 
     44\item 
     45  {\em visibility along a line} 
     46  \begin{enumerate} 
     47  \item line 
     48  \item ray (origin + direction) 
     49  \end{enumerate} 
     50  \newpage 
     51\item 
     52  {\em visibility from a point} ({\em from-point visibility}) 
     53  \begin{enumerate} 
     54  \item point 
     55  \item point + restricted set of rays 
     56    \begin{enumerate} 
     57    \item point + raster image (discrete form) 
     58    \item point + beam (continuous form) 
     59    \end{enumerate} 
     60  \end{enumerate} 
     61 
     62   
     63\item 
     64  {\em visibility from a line segment} ({\em from-segment visibility}) 
     65  \begin{enumerate} 
     66  \item line segment 
     67  \item line segment + restricted set of rays 
     68  \end{enumerate} 
     69   
     70\item 
     71  {\em visibility from a polygon} ({\em from-polygon visibility}) 
     72  \begin{enumerate} 
     73  \item polygon 
     74  \item polygon + restricted set of rays 
     75  \end{enumerate} 
     76   
     77\item 
     78  {\em visibility from a region} ({\em from-region visibility}) 
     79  \begin{enumerate} 
     80    \item region 
     81    \item region + restricted set of rays 
     82  \end{enumerate} 
     83 
     84\item 
     85  {\em global visibility} 
     86  \begin{enumerate} 
     87    \item no further input (all rays in the scene) 
     88    \item restricted set of rays 
     89  \end{enumerate} 
     90\end{enumerate} 
     91   
     92 The domain restrictions can be given independently of the dimension 
     93of the scene, but the impact of the restrictions differs depending on 
     94the scene dimension. For example, visibility from a polygon is 
     95equivalent to visibility from a (polygonal) region in 2D, but not in 
     963D. 
     97 
     98%***************************************************************************** 
     99 
     100\section{Dimension of the problem-relevant line set} 
     101 
     102 The six domains of visibility problems stated in 
     103Section~\ref{sec:prob_domain} can be characterized by the {\em 
     104problem-relevant line set} denoted ${\cal L}_R$. We give a 
     105classification of visibility problems according to the dimension of 
     106the problem-relevant line set. We discuss why this classification is 
     107important for understanding the nature of the given visibility problem 
     108and for identifying its relation to other problems. 
     109 
     110 
     111 For the following discussion we assume that a line in {\em primal 
     112space} can be mapped to a point in {\em line space}. For purposes of 
     113the classification we define the line space as a vector space where a 
     114point corresponds to a line in the primal space\footnote{A classical 
     115mathematical definition says: Line space is a direct product of two 
     116Hilbert spaces~\cite{Weisstein:1999:CCE}. However, this definition 
     117differs from the common understanding of line space in computer 
     118graphics~\cite{Durand99-phd}}. 
     119 
     120 
     121 
     122\subsection{Parametrization of lines in 2D} 
     123 
     124 There are two independent parameters that specify a 2D line and thus 
     125the corresponding set of lines is two-dimensional. There is a natural 
     126duality between lines and points in 2D. For example a line expressed 
     127as: $l:y=ax+c$ is dual to a point $p=(-c,a)$. This particular duality 
     128cannot handle vertical lines. See Figure~\ref{fig:duality2d} for an 
     129example of other dual mappings in the plane.  To avoid the singularity 
     130in the mapping, a line $l:ax+by+c=0$ can be represented as a point 
     131$p_l=(a,b,c)$ in 2D projective space ${\cal 
     132P}^2$~\cite{Stolfi:1991:OPG}. Multiplying $p_l$ by a non-zero scalar 
     133we obtain a vector that represents the same line $l$. More details 
     134about this singularity-free mapping will be discussed in 
     135Chapter~\ref{chap:vfr2d}. 
     136 
     137 
     138\begin{figure}[!htb] 
     139\centerline{ 
     140\includegraphics[width=0.9\textwidth,draft=\DRAFTFIGS]{figs/duality2d}} 
     141\caption{Duality between points and lines in 2D.} 
     142\label{fig:duality2d} 
     143\end{figure} 
     144 
     145 
     146 
     147 
     148 
     149 To sum up: In 2D there are two degrees of freedom in description of a 
     150line and the corresponding line space is two-dimensional. The 
     151problem-relevant line set ${\cal L}_R$ then forms a $k$-dimensional 
     152subset of ${\cal P}^2$, where $0\leq k \leq 2$. An illustration of the 
     153concept of the problem-relevant line set is depicted in 
     154Figure~\ref{fig:classes}. 
     155 
     156 
     157\begin{figure}[htb] 
     158\centerline{ 
     159\includegraphics[width=0.8\textwidth,draft=\DRAFTFIGS]{figs/classes}} 
     160\caption{The problem-relevant set of lines in 2D. The ${\cal L}_R$ for 
     161visibility along a line is formed by a single point that is a mapping 
     162of the given line. The ${\cal L}_R$ for visibility from a point $p$ is 
     163formed by points lying on a line. This line is a dual mapping of the 
     164point $p$. ${\cal L}_R$ for visibility from a line segment is formed 
     165by a 2D region bounded by dual mappings of endpoints of the given 
     166segment.} 
     167\label{fig:classes} 
     168\end{figure} 
     169 
     170 
     171\subsection{Parametrization of lines in 3D} 
     172 
     173 
     174Lines in 3D form a four-parametric space~\cite{p-rsls-97}. A line 
     175intersecting a given scene can be described by two points on a sphere 
     176enclosing the scene. Since the surface of the sphere is a two 
     177parametric space, we need four parameters to describe the line. 
     178 
     179 The {\em two plane parametrization} of 3D lines describes a line by 
     180points of intersection with the given two 
     181planes~\cite{Gu:1997:PGT}. This parametrization exhibits a singularity 
     182since it cannot describe lines parallel to these planes. See 
     183Figure~\ref{fig:3dlines} for illustrations of the spherical and the 
     184two plane parameterizations. 
     185 
     186 
     187\begin{figure}[htb] 
     188\centerline{ 
     189\includegraphics[width=0.78\textwidth,draft=\DRAFTFIGS]{figs/3dlines}} 
     190\caption{Parametrization of lines in 3D. (left) A line can be 
     191  described by two points on a sphere enclosing the scene. (right) The 
     192  two plane parametrization describes a line by point of intersection 
     193  with two given planes.} 
     194\label{fig:3dlines} 
     195\end{figure} 
     196 
     197 Another common parametrization of 3D lines are the {\em \plucker 
     198coordinates}. \plucker coordinates of an oriented 3D line are a six 
     199tuple that can be understood as a point in 5D oriented projective 
     200space~\cite{Stolfi:1991:OPG}. There are six coordinates in \plucker 
     201representation of a line although we know that the ${\cal L}_R$ is 
     202four-dimensional. This can be explained as follows: 
     203 
     204\begin{itemize} 
     205  \item Firstly, \plucker coordinates are {\em homogeneous 
     206    coordinates} of a 5D point. By multiplication of the coordinates 
     207    by any positive scalar we get a mapping of the same line. 
     208  \item Secondly, only 4D subset of the 5D oriented projective space 
     209  corresponds to real lines. This subset is a 4D ruled quadric called 
     210  the {\em \plucker quadric} or the {\em Grassman 
     211  manifold}~\cite{Stolfi:1991:OPG,Pu98-DSGIV}. 
     212\end{itemize} 
     213 
     214 Although the \plucker coordinates need more coefficients they have no 
     215singularity and preserve some linearities: lines intersecting a set of  
     216lines in 3D correspond to an intersection of 5D hyperplanes. More details 
     217on \plucker coordinates will be discussed in 
     218Chapters~\ref{chap:vfr25d} and~\ref{chap:vfr3d} where they are used to 
     219solve the from-region visibility problem. 
     220 
     221  To sum up: In 3D there are four degrees of freedom in the 
     222description of a line and thus the corresponding line space is 
     223four-dimensional. Fixing certain line parameters (e.g. direction) the 
     224problem-relevant line set, denoted ${\cal L}_R$, forms a 
     225$k$-dimensional subset of ${\cal P}^4$, where $0\leq k \leq 4$. 
     226 
     227 
     228\subsection{Visibility along a line} 
     229 
     230 The simplest visibility problems deal with visibility along a single 
     231line. The problem-relevant line set is zero-dimensional, i.e. it is 
     232fully specified by the given line. A typical example of a visibility 
     233along a line problem is {\em ray shooting}. 
     234 
     235 A similar problem to ray shooting is the {\em point-to-point} 
     236visibility.  The  point-to-point visibility determines whether the 
     237line segment between two points is occluded, i.e. it has an 
     238intersection with an opaque object in the scene. Point-to-point 
     239visibility provides a visibility classification (answer 1a), whereas 
     240ray shooting determines a visible object (answer 2a) and/or a point of 
     241intersection (answer 3a). Note that the {\em point-to-point} 
     242visibility can be solved easily by means of ray shooting. Another 
     243constructive visibility along a line problem is determining the {\em 
     244maximal free line segments} on a given line. See Figure~\ref{fig:val} 
     245for an illustration of typical visibility along a line problems. 
     246 
     247\begin{figure}[htb] 
     248\centerline{ 
     249\includegraphics[width=0.85\textwidth,draft=\DRAFTFIGS]{figs/val}} 
     250\caption{Visibility along a line. (left) Ray shooting. (center) Point-to-point visibility. (right) Maximal free line segments between two points.} 
     251\label{fig:val} 
     252\end{figure} 
     253 
     254 
     255\subsection{Visibility from a point} 
     256 
     257 Lines intersecting a point in 3D can be described  by two 
     258parameters. For example the lines can be expressed by an intersection 
     259with a unit sphere centered at the given point. The most common 
     260parametrization describes a line by a point of intersection with a 
     261given viewport. Note that this parametrization accounts only for a 
     262subset of lines that intersect the viewport (see Figure~\ref{fig:vfp}). 
     263 
     264\begin{figure}[htb] 
     265\centerline{ 
     266\includegraphics[width=0.6\textwidth,draft=\DRAFTFIGS]{figs/vfp}} 
     267\caption{Visibility from a point. Lines intersecting a point can be described by a 
     268  point of intersection with the given viewport.} 
     269\label{fig:vfp} 
     270\end{figure} 
     271 
     272 In 3D the problem-relevant line set ${\cal L}_R$ is a 2D subset of 
     273the 4D line space. In 2D the ${\cal L}_R$ is a 1D subset of the 2D 
     274line space. The typical visibility from a point problem is the visible 
     275surface determination. Due to its importance the visible surface 
     276determination is covered by the majority of existing visibility 
     277algorithms. Other visibility from a point problem is the construction 
     278of the {\em visibility map} or the {\em point-to-region visibility} 
     279that classifies a region as visible, invisible, or partially visible 
     280with respect to the given point. 
     281 
     282 
     283\subsection{Visibility from a line segment} 
     284 
     285 Lines intersecting a line segment in 3D can be described by three 
     286parameters. One parameter fixes the intersection of the line with the 
     287segment the other two express the direction of the line. The 
     288problem-relevant line set ${\cal L}_R$ is three-dimensional and it can 
     289be understood as a 2D cross section of ${\cal L}_R$  swept according 
     290to the translation on the given line segment (see 
     291Figure~\ref{fig:vls}). 
     292 
     293 
     294\begin{figure}[htb] 
     295\centerline{ 
     296\includegraphics[width=0.8\textwidth,draft=\DRAFTFIGS]{figs/vls}} 
     297\caption{Visibility from a line segment. (left) Line segment, a 
     298  spherical object $O$, and its projections $O^*_0$, $O^*_{0.5}$, $O^*_{1}$ 
     299  with respect to the three points on the line segment. (right) 
     300  A possible parametrization of lines that stacks up 2D planes. 
     301  Each plane corresponds to mappings of lines intersecting a given 
     302  point on the line segment.} 
     303\label{fig:vls} 
     304\end{figure} 
     305 
     306In 2D lines intersecting a line segment form a two-dimensional 
     307problem-relevant line set. Thus for the 2D case the ${\cal L}_R$ is a 
     308two-dimensional subset of 2D line space. 
     309 
     310 
     311\subsection{Visibility from a region} 
     312 
     313 Visibility from a region (or from-region visibility) involves the 
     314most general visibility problems. In 3D the ${\cal L}_R$ is a 4D 
     315subset of the 4D line space. In 2D the ${\cal L}_R$ is a 2D subset of 
     316the 2D line space. Consequently, in the proposed classification 
     317visibility from a region in 2D is equivalent to visibility from a line 
     318segment in 2D. 
     319 
     320 A typical visibility from a region problem is the problem of {\em 
     321region-to-region} visibility that aims to determine if the two given 
     322regions in the scene are visible, invisible, or partially visible (see 
     323Figure~\ref{fig:vfr}). Another visibility from region problem is the 
     324computation of a {\em potentially visible set} (PVS) with respect to a 
     325given view cell. The PVS consists of a set of objects that are 
     326potentially visible from any point inside the view cell. Further 
     327visibility from a region problems include computing form factors 
     328between two polygons, soft shadow algorithms or discontinuity meshing. 
     329 
     330 
     331\begin{figure}[htb] 
     332\centerline{ 
     333\includegraphics[width=0.6\textwidth,draft=\DRAFTFIGS]{figs/vfr}} 
     334\caption{Visibility from a region --- an example of the region-to-region 
     335  visibility. Two regions and two occluders $A$, $B$ 
     336  in a 2D scene.  In line space the region-to-region visibility can be 
     337  solved by subtracting the sets of lines $A^*$ and $B^*$ 
     338  intersecting objects $A$ and $B$ from the lines intersecting both 
     339  regions.} 
     340\label{fig:vfr} 
     341\end{figure} 
     342 
     343 
     344\subsection{Global visibility} 
     345 
     346 According to the classification the global visibility problems can be 
     347seen as an extension of the from-region visibility problems. The 
     348dimension of the problem-relevant line set is the same ($k=2$ for 2D 
     349and $k=4$ for 3D scenes). Nevertheless, the global visibility problems 
     350typically deal with much larger set of rays, i.e.  all rays that 
     351penetrate the scene. Additionally, there is no given set of reference 
     352points from which visibility is studied and hence there is no given 
     353priority ordering of objects along each particular line from ${\cal 
     354L}_R$. Therefore an additional parameter must be used to describe 
     355visibility (visible object) along each ray. 
     356 
     357 
     358\subsection{Summary} 
     359 
     360 The classification of visibility problems according to the dimension 
     361of the problem-relevant line set is summarized in 
     362Table~\ref{table:classification3D}.  This classification provides 
     363means for understanding how difficult it is to compute, describe, and 
     364maintain visibility for a particular class of problems. For example a 
     365data structure representing the visible or occluded parts of the scene 
     366for the visibility from a point problem needs to partition a 2D ${\cal 
     367L}_R$ into visible and occluded sets of lines. This observation 
     368conforms with the traditional visible surface algorithms -- they 
     369partition a 2D viewport into empty/nonempty regions and associate each 
     370nonempty regions (pixels) with a visible object. In this case the 
     371viewport represents the ${\cal L}_R$ as each point of the viewport 
     372corresponds to a line through that point. To analytically describe 
     373visibility from a region a subdivision of 4D ${\cal L}_R$ should be 
     374performed. This is much more difficult than the 2D 
     375subdivision. Moreover the description of visibility from a region 
     376involves non-linear subdivisions of both primal space and line space 
     377even for polygonal scenes~\cite{Teller:1992:CAA,Durand99-phd}. 
     378 
     379\begin{table*}[htb] 
     380\begin{small} 
     381\begin{center} 
     382\begin{tabular}{|l|c|l|} 
     383\hline 
     384\multicolumn{3}{|c|}{2D} \\ 
     385\hline 
     386\mc{domain} & $d({\cal L}_R)$ & \mc{problems} \\ 
     387\hline 
     388\hline 
     389\begin{tabular}{l}visibility along a line\end{tabular}         & 0 &  \begin{tabular}{l}ray shooting, point-to-point visibility\end{tabular}\\ 
     390\hline 
     391\begin{tabular}{l}visibility from a point\end{tabular}         & 1 &  \begin{tabular}{l}view around a point, point-to-region visibility\end{tabular}\\ 
     392\hline 
     393\begin{tabular}{l} visibility from a line segment \\ visibility from region \\ global visibility \end{tabular} 
     394   & 2 & \begin{tabular}{l} region-to-region visibility, PVS \end{tabular}\\ 
     395\hline 
     396\hline 
     397\multicolumn{3}{|c|}{3D} \\ 
     398\hline 
     399\mc{domain} & $d({\cal L}_R)$ & \mc{problems} \\ 
     400\hline 
     401\hline 
     402\begin{tabular}{l}visibility along a line\end{tabular}  & 0 & \begin{tabular}{l} ray shooting, point-to-point visibility \end{tabular}\\ 
     403\hline 
     404\begin{tabular}{l}from point in a surface\end{tabular}  & 1 & \begin{tabular}{l} see visibility from point in 2D \end{tabular}\\ 
     405\hline 
     406\begin{tabular}{l}visibility from a point\end{tabular}  & 2 & \begin{tabular}{l} visible (hidden) surfaces, point-to-region visibility,\\ 
     407                                      visibility map, hard shadows 
     408                               \end{tabular} \\ 
     409\hline 
     410\begin{tabular}{l}visibility from a line segment\end{tabular} & 3 & \begin{tabular}{l} segment-to-region visibility (rare) \end{tabular}\\ 
     411\hline 
     412\begin{tabular}{l}visibility from a region\\global visibility\end{tabular}       & 4 & \begin{tabular}{l} region-region visibility, PVS, aspect graph,\\ 
     413                                            soft shadows, discontinuity meshing 
     414                                     \end{tabular} \\ 
     415 
     416\hline   
     417\end{tabular} 
     418\end{center} 
     419\end{small} 
     420\caption{Classification of visibility problems in 2D and 3D according 
     421to the dimension of the problem-relevant line set.} 
     422\label{table:classification3D} 
     423\end{table*} 
     424 
     425 
     426 
     427 
     428 
     429\section{Classification of visibility algorithms} 
     430 
     431 
     432The taxonomy of visibility problems groups similar visibility problems 
     433in the same class.  A visibility problem can be solved by means of 
     434various visibility algorithms. A visibility algorithm poses further 
     435restrictions on the input and output data. These restrictions can be 
     436seen as a more precise definition of the visibility problem that is 
     437solved by the algorithm. 
     438 
     439 Above we classified visibility problems according to the problem 
     440domain and the desired answers. In this section we provide a 
     441classification of visibility algorithms according to other 
     442important criteria characterizing a particular visibility algorithm. 
     443 
     444 
     445\subsection{Scene restrictions} 
     446\label{sec:scene_restrictions} 
     447 
     448Visibility algorithms can be classified according to the restrictions 
     449they pose on the scene description.  The type of the scene description 
     450influences the difficulty of solving the given problem: it is simpler 
     451to implement an algorithm computing a visibility map for scenes 
     452consisting of triangles than for scenes with NURBS surfaces. We list 
     453common restrictions on the scene primitives suitable for visibility 
     454computations: 
     455 
     456 
     457\begin{itemize} 
     458\item 
     459  triangles, convex polygons, concave polygons, 
     460   
     461\item 
     462  volumetric data, 
     463 
     464\item 
     465  points, 
     466 
     467\item 
     468  general parametric, implicit, or procedural surfaces. 
     469 
     470\end{itemize} 
     471 
     472Some attributes of scenes objects further increase the complexity of the visibility computation: 
     473 
     474\begin{itemize} 
     475 
     476\item 
     477  transparent objects, 
     478 
     479\item 
     480  dynamic objects. 
     481 
     482\end{itemize} 
     483 
     484The majority of analytic visibility algorithms deals with static 
     485polygonal scenes without transparency. The polygons are often 
     486subdivided into triangles for easier manipulation and representation. 
     487 
     488\subsection{Accuracy} 
     489\label{sec:accuracy} 
     490 
     491 Visibility algorithms can be classified according to the accuracy of 
     492the result as: 
     493 
     494\begin{itemize} 
     495\item exact, 
     496\item conservative, 
     497\item aggressive, 
     498\item approximate. 
     499\end{itemize} 
     500 
     501 
     502 An exact algorithm provides an exact analytic result for the given 
     503problem (in practice however this result is typically influenced by 
     504the finite precision of the floating point arithmetics). A 
     505conservative algorithm overestimates visibility, i.e. it never 
     506misses any visible object, surface or point. An aggressive algorithm 
     507always underestimates visibility, i.e. it never reports an invisible 
     508object, surface or point as visible.  An approximate algorithm 
     509provides only an approximation of the result, i.e. it can overestimate 
     510visibility for one input and underestimate visibility for another 
     511input. 
     512 
     513 The classification according to the accuracy is best illustrated on 
     514computing PVS: an exact algorithm computes an exact PVS. A 
     515conservative algorithm computes a superset of the exact PVS.  An 
     516aggressive algorithm determines a subset of the exact PVS.  An 
     517approximate algorithm computes an approximation to the exact PVS that 
     518is neither its subset or its superset for all possible inputs. 
     519 
     520 
     521\subsection{Solution space} 
     522 
     523\label{sec:solspace} 
     524 
     525 The solution space is the domain in which the algorithm determines 
     526the desired result. Note that the solution space does not need to 
     527match the domain of the result. 
     528 
     529The algorithms can be classified as: 
     530 
     531\begin{itemize} 
     532  \item discrete, 
     533  \item continuous, 
     534  \item hybrid. 
     535\end{itemize} 
     536 
     537 A discrete algorithm solves the problem using a discrete solution 
     538space; the solution is typically an approximation of the result. A 
     539continuous algorithm works in a continuous domain and often computes an 
     540analytic solution to the given problem. A hybrid algorithm uses both 
     541the discrete and the continuous solution space. 
     542 
     543 The classification according to the solution space is easily 
     544demonstrated on visible surface algorithms (these algorithms will be 
     545discussed in Section~\ref{chap:traditional}). The 
     546z-buffer~\cite{Catmull:1975:CDC} is a common example of a discrete 
     547algorithm. The Weiler-Atherton algorithm~\cite{Weiler:1977:HSR} is an 
     548example of a continuous one. A hybrid solution space is used by 
     549scan-line algorithms that solve the problem in discrete steps 
     550(scan-lines) and for each step they provide a continuous solution 
     551(spans). 
     552 
     553Further classification reflects the semantics of the solution 
     554space. According to this criteria we can classify the algorithms as: 
     555 
     556\begin{itemize} 
     557  \item primal space (object space), 
     558  \item line space, 
     559    \begin{itemize} 
     560    \item image space, 
     561    \item general, 
     562    \end{itemize} 
     563  \item hybrid. 
     564\end{itemize} 
     565 
     566 A primal space algorithm solves the problem by studying the 
     567visibility between objects without a transformation to a different 
     568solution space. A line space algorithm studies visibility using a 
     569transformation of the problem to line space. Image space algorithms 
     570can be seen as an important subclass of line space algorithms for 
     571solving visibility from a point problems in 3D. These algorithms cover 
     572all visible surface algorithms and many visibility culling 
     573algorithms. They solve visibility in a given image plane that 
     574represents the problem-relevant line set ${\cal L}_R$ --- each ray 
     575originating at the viewpoint corresponds to a point in the image plane. 
     576 
     577 The described classification differs from the sometimes mentioned 
     578understanding of image space and object space algorithms that 
     579incorrectly considers all image space algorithms discrete and all 
     580object space algorithms continuous. 
     581 
     582 
     583%***************************************************************************** 
     584 
     585\section{Visibility algorithm design} 
     586 
     587 Visibility algorithm design can be decoupled into a series of 
     588important design decisions. The first step is to clearly formulate a 
     589problem that should be solved by the algorithm. The  taxonomy stated 
     590above can help to understand the difficulty of solving the given 
     591problem and its relationship to other visibility problems in computer 
     592graphics. The following sections summarize important steps in the 
     593design of a visibility algorithm and discuss some commonly used 
     594techniques. 
     595 
     596 
     597\subsection{Scene structuring} 
     598 
     599 We discuss two issues dealing with structuring of the scene: 
     600identifying occluders and occludees, and spatial data structures for 
     601scene description. 
     602 
     603\subsubsection{Occluders and occludees} 
     604%occluders, occludees,  
     605 
     606Many visibility algorithms restructure the scene description to 
     607distinguish between {\em occluders} and {\em occludees}.  Occluders 
     608are objects that cause changes in visibility (occlusion). Occludees 
     609are objects that do not cause occlusion, but are sensitive to 
     610visibility changes. In other words the algorithm studies visibility of 
     611occludees with respect to occluders. 
     612 
     613The concept of occluders and occludees is used to increase the 
     614performance of the algorithm in both the running time and the accuracy 
     615of the algorithm by reducing the number of primitives used for 
     616visibility computations (the performance measures of visibility 
     617algorithms will be discussed in 
     618Section~\ref{sec:performance}). Typically, the number of occluders and 
     619occludees is significantly smaller than the total number of objects in 
     620the scene. Additionally, both the occluders and the occludees can be 
     621accompanied with a topological (connectivity) information that might 
     622be necessary for an efficient functionality of the algorithm. 
     623 
     624 The concept of occluders is applicable to most visibility 
     625algorithms. The concept of occludees is useful for algorithms 
     626providing answers (1) and (2) according to the taxonomy of 
     627Section~\ref{sec:answers}. Some visibility algorithms do not 
     628distinguish between occluders and occludees at all. For example all 
     629traditional visible surface algorithms use all scene objects as both 
     630occluders and occludees. 
     631 
     632 Both the occluders and the occludees can be represented by {\em 
     633virtual objects} constructed from the scene primitives: the occluders 
     634as simplified inscribed objects, occludees as simplified circumscribed 
     635objects such as bounding boxes. Algorithms can be classified according 
     636to the type of occluders they deal with. The classification follows 
     637the scene restrictions discussed in 
     638Section~\ref{sec:scene_restrictions} and adds classes specific to 
     639occluder restrictions: 
     640 
     641\begin{itemize} 
     642\item 
     643  vertical prisms, 
     644\item 
     645  axis-aligned polygons, 
     646\item 
     647  axis-aligned rectangles. 
     648\end{itemize} 
     649   
     650 The vertical prisms that are specifically important for computing 
     651visibility in \m25d scenes. Some visibility algorithms can deal only 
     652with axis-aligned polygons or even more restrictive axis-aligned 
     653rectangles. 
     654 
     655 
     656\begin{figure}[htb] 
     657\centerline{ 
     658\includegraphics[width=0.7\textwidth,draft=\DRAFTIMAGES]{images/houses}} 
     659\caption{Occluders in an urban scene. In urban scenes the occluders 
     660can be considered vertical prisms erected above the ground.} 
     661\label{fig:houses} 
     662\end{figure} 
     663 
     664Other important criteria for evaluating algorithms according to 
     665occluder restrictions include: 
     666 
     667 
     668\begin{itemize} 
     669\item 
     670  connectivity information, 
     671\item 
     672  intersecting occluders. 
     673\end{itemize} 
     674 
     675 
     676The explicit knowledge of the connectivity is crucial for efficient 
     677performance of some visibility algorithms (performance measures will 
     678be discussed in the Section~\ref{sec:performance}). Intersecting 
     679occluders cannot be handled properly by some visibility algorithms. 
     680In such a case the possible occluder intersections should be resolved 
     681in preprocessing. 
     682 
     683 A similar classification can be applied to occludees. However, the 
     684visibility algorithms typically pose less restrictions on occludees 
     685since they are not used to describe visibility but only to check 
     686visibility with respect to the description provided by the occluders. 
     687 
     688 
     689%occluder selection, occluder LOD, virtual occluders, 
     690 
     691\subsubsection{Scene description} 
     692 
     693 The scene is typically represented by a collection of objects. For 
     694purposes of visibility computations it can be advantageous to transform 
     695the object centered representation to a spatial representation by 
     696means of a spatial data structure.  For example the scene can be 
     697represented by an octree where full voxels correspond to opaque parts 
     698of the scene. Such a data structure is then used as an input to the 
     699visibility algorithm. The spatial data structures for the scene 
     700description are used for the following reasons: 
     701 
     702\begin{itemize} 
     703 
     704\item {\em Regularity}. A spatial data structure typically provides a 
     705  regular description of the scene that avoids complicated 
     706  configurations or overly detailed input. Furthermore, the 
     707  representation can be rather independent of the total scene 
     708  complexity. 
     709   
     710\item {\em Efficiency}. The algorithm can be more efficient in both 
     711  the running time and the accuracy of the result. 
     712 
     713\end{itemize} 
     714 
     715 
     716Additionally, spatial data structures can be applied to structure the 
     717solution space and/or to represent the desired solution. Another 
     718application of spatial data structures is the acceleration of the 
     719algorithm by providing spatial indexing. These applications of spatial 
     720data structures will be discussed in 
     721Sections~\ref{sec:solution_space_ds} 
     722and~\ref{sec:acceleration_ds}. Note that a visibility algorithm can 
     723use a single data structure for all three purposes (scene 
     724structuring, solution space structuring, and spatial indexing) while 
     725another visibility algorithm can use three conceptually different data 
     726structures. 
     727 
     728 
     729% gernots alg. 
     730%used as solution space DS and/or acceleration DS 
     731 
     732\subsection{Solution space data structures} 
     733\label{sec:solution_space_ds} 
     734 
     735A solution space data structure is used to maintain an intermediate 
     736result during the operation of the algorithm and it is used to 
     737generate the result of the algorithm. We distinguish between the 
     738following solution space data structures: 
     739 
     740\begin{itemize} 
     741 
     742\item general data structures 
     743 
     744  single value (ray shooting), winged edge, active edge table, etc. 
     745   
     746\item primal space (spatial) data structures 
     747   
     748  uniform grid, BSP tree (shadow volumes), 
     749  bounding volume hierarchy, kD-tree, octree, etc. 
     750   
     751\item image space data structures 
     752 
     753  2D uniform grid (shadow map), 2D BSP tree, quadtree, kD-tree, etc. 
     754 
     755\item line space data structures 
     756 
     757  regular grid, kD-tree, BSP tree, etc. 
     758   
     759\end{itemize} 
     760 
     761 The image space data structures can be considered a special case of 
     762the line space data structures since a point in the image represents a 
     763ray through that point (see also Section~\ref{sec:solspace}). 
     764 
     765 If the dimension of the solution space matches the dimension of the 
     766problem-relevant line set, the visibility problem can often be solved 
     767with high accuracy by a single sweep through the scene. If the 
     768dimensions do not match, the algorithm typically needs more passes to 
     769compute a result with satisfying accuracy~\cite{EVL-2000-60,wonka00} 
     770or neglects some visibility effects altogether~\cite{EVL-2000-59}. 
     771 
     772 
     773%ray shooting none 
     774%visible surface algorithms - list of scan-line intersections, BSP tree, 
     775% z-buffer (graphics hardware) 
     776%shadow computation shadow volumes, BSP tree, shadow map (graphics hardware) 
     777%PVS for view cell - occlusion tree 
     778 
     779 
     780\subsection{Performance} 
     781\label{sec:performance} 
     782 
     783%output sensitivity, memory consumption, running time, scalability 
     784 
     785 
     786The performance of a visibility algorithm can be evaluated by measuring 
     787the quality of the result, the running time and the memory consumption. 
     788In this section we discuss several concepts related to these 
     789performance criteria. 
     790 
     791 
     792\subsubsection{Quality of the result} 
     793 
     794 One of the important performance measures of a visibility algorithm 
     795is the quality of the result. The quality measure depends on the type 
     796of the answer to the problem. Generally, the quality of the result 
     797can be expressed as a distance from an exact result in the solution 
     798space.  Such a quality measure can be seen as a more precise 
     799expression of the accuracy of the algorithm discussed in 
     800Section~\ref{sec:accuracy}. 
     801 
     802For example a quality measure of algorithms computing a PVS can be 
     803expressed by the {\em relative overestimation} and the {\em relative 
     804underestimation} of the PVS with respect to the exact PVS.  We can 
     805define a quality measure of an algorithm $A$ on input $I$ as a tuple 
     806$\mbi{Q}^A(I)$: 
     807 
     808\begin{eqnarray} 
     809  \mbi{Q}^A(I) & = & (Q^A_o(I), Q^A_u(I)), \qquad  I \in {\cal D} \\ 
     810  Q^A_o(I) & = & {|S^A(I) \setminus S^{\cal E}(I)| \over |S^{\cal E}(I)|} \\ 
     811  Q^A_u(I) & = & {|S^{\cal E}(I) \setminus S^A(I) | \over |S^{\cal E}(I)|} 
     812\end{eqnarray} 
     813 
     814where $I$ is an input from the input domain ${\cal D}$, $S^A(I)$ is 
     815the PVS determined by the algorithm $A$ for input $I$ and $S^{\cal 
     816E}(I)$ is the exact PVS for the given input. $Q^A_o(I)$ expresses the 
     817{\em relative overestimation} of the PVS, $Q^A_u(I)$ is the {\em 
     818relative underestimation}. 
     819 
     820The expected quality of the algorithm over all possible inputs can be 
     821given as: 
     822 
     823\begin{eqnarray} 
     824Q^A & = & E[\| \mbi{Q}^A(I) \|] \\ 
     825    & = & \sum_{\forall I \in {\cal D}} f(I).\sqrt{Q^A_o(I)^2 + Q^A_o(I)^2} 
     826\end{eqnarray} 
     827 
     828where f(I) is the probability density function expressing the 
     829probability of occurrence of input $I$. The quality measure 
     830$\mbi{Q}^A(I)$ can be used to classify a PVS algorithm into one of the 
     831four accuracy classes according to Section~\ref{sec:accuracy}: 
     832 
     833\begin{enumerate} 
     834\item exact\\ 
     835  $\forall I \in {\cal D} :Q_o^A(I) = 0 \wedge Q_u^A(I) = 0$ 
     836\item conservative\\ 
     837  $\forall I \in {\cal D} : Q_o^A(I) \geq 0 \wedge Q_u^A(I) = 0$ 
     838\item aggressive \\ 
     839  $\forall I \in {\cal D} : Q_o^A(I) = 0 \wedge Q_u^A(I) \geq 0$ 
     840\item approximate \\ 
     841  $\qquad \exists I_j, I_k \in {\cal D}: Q_o^A(I_j) > 0 \wedge Q_u^A(I_k) > 0$ 
     842\end{enumerate} 
     843 
     844 
     845 
     846\subsubsection{Scalability} 
     847 
     848 Scalability expresses the ability of the visibility algorithm to cope 
     849with larger inputs. A more precise definition of scalability of an 
     850algorithm depends on the problem for which the algorithm is 
     851designed. The scalability of an algorithm can be studied with respect 
     852to the size of the scene (e.g. number of scene objects). Another 
     853measure might consider the dependence of the algorithm on the number 
     854of only the visible objects. Scalability can also be studied 
     855according to the given domain restrictions, e.g. volume of the view 
     856cell. 
     857 
     858 A well designed visibility algorithm should be scalable with respect 
     859to the number of structural changes or discontinuities of 
     860visibility. Furthermore, its performance should be given by the 
     861complexity of the visible part of the scene. These two important 
     862measures of scalability of an algorithm are discussed in the next two 
     863sections. 
     864 
     865\subsubsection{Use of coherence} 
     866 
     867 Scenes in computer graphics typically consist of objects whose 
     868properties vary smoothly from one part to another. A view of such a 
     869scene contains regions of smooth changes (changes in color, depth, 
     870texture,etc.) and discontinuities that let us distinguish between 
     871objects. The degree to which the scene or its projection exhibit local 
     872similarities is called {\em coherence}~\cite{Foley90}. 
     873 
     874 Coherence can be exploited by reusing calculations made for one part 
     875of the scene for nearby parts. Algorithms exploiting coherence are 
     876typically more efficient than algorithms computing the result from the 
     877scratch. 
     878 
     879 Sutherland et al.~\cite{Sutherland:1974:CTH} identified several 
     880different types of coherence in the context of visible surface 
     881algorithms. We simplify the classification proposed by Sutherland et 
     882al. to reflect general visibility problems and distinguish between the 
     883following three types of {\em visibility coherence}: 
     884 
     885\begin{itemize} 
     886 
     887\item {\em Spatial coherence}. Visibility of points in space tends to 
     888  be coherent in the sense that the visible part of the scene consists 
     889  of compact sets (regions) of visible and invisible points. We can 
     890  reuse calculations made for a given region for the neighboring 
     891  regions or its subregions. 
     892 
     893\item {\em Line-space coherence}. Sets of similar rays tend to have the 
     894  same visibility classification, i.e. the rays intersect the same 
     895  object. We can reuse calculations for the given set of rays for its 
     896  subsets or the sets of nearby rays. 
     897 
     898\item {\em Temporal coherence}. Visibility at two successive moments is 
     899  likely to be similar despite small changes in the scene or a 
     900  region/point of interest. Calculations made for one frame can be 
     901  reused for the next frame in a sequence. 
     902 
     903\end{itemize} 
     904  
     905 The degree to which the algorithm exploits various types of coherence 
     906is one of the major design paradigms in research of new visibility 
     907algorithms. The importance of exploiting coherence is emphasized by 
     908the large amount of data that need to be processed by the current 
     909rendering algorithms. 
     910 
     911 
     912\subsubsection{Output sensitivity} 
     913 
     914 
     915An algorithm is said to be {\em output-sensitive} if its running time 
     916is sensitive to the size of output. In the computer graphics community 
     917the term output-sensitive algorithm is used in a broader meaning than 
     918in computational geometry~\cite{berg:97}. The attention is paid to a 
     919practical usage of the algorithm, i.e. to an efficient implementation 
     920in terms of the practical average case performance. The algorithms are 
     921usually evaluated experimentally using test data and measuring the 
     922running time and the size of output of the algorithm. The formal 
     923average case analysis is usually not carried out for the following two 
     924reasons: 
     925 
     926\begin{enumerate} 
     927 
     928\item {\em The algorithm is too obscured}. Visibility algorithms 
     929exploit data structures that are built according to various heuristics 
     930and it is difficult to derive proper bounds even on the expected size 
     931of these supporting data structures. 
     932 
     933\item {\em It is difficult to properly model the input data}. In 
     934general it is difficult to create a reasonable model that captures 
     935properties of real world scenes as well as the probability of 
     936occurrence of a particular configuration. 
     937 
     938\end{enumerate} 
     939 
     940 A visibility algorithm can often be divided into the {\em offline} 
     941phase and the {\em online} phase. The offline phase is also called 
     942preprocessing. The preprocessing is often amortized over many 
     943executions of the algorithm and therefore it is advantageous to 
     944express it separately from the online running time. 
     945 
     946 For example an ideal output-sensitive visible surface algorithm runs 
     947in $O(n\log n + k^2)$, where $n$ is the number of scene polygons (size 
     948of input) and $k$ is the number of visible polygons (in the worst case 
     949$k$ visible polygons induce $O(k^2)$ visible polygon fragments). 
     950 
     951 
     952 
     953\subsubsection{Acceleration data structures} 
     954\label{sec:acceleration_ds} 
     955 
     956 Acceleration data structures are often used to achieve the performance 
     957goals of a visibility algorithm. These data structures allow efficient 
     958point location, proximity queries, or scene traversal required by many 
     959visibility algorithms. 
     960 
     961 With a few exceptions the acceleration data structures provide a {\em 
     962spatial index} for the scene by means of a spatial data structure. 
     963The spatial data structures group scene objects according to the 
     964spatial proximity. On the contrary line space data structures group 
     965rays according to their proximity in line space. 
     966 
     967The common acceleration data structures can be divided into the 
     968following  categories: 
     969 
     970\begin{itemize} 
     971\item Spatial data structures 
     972  \begin{itemize} 
     973  \item {\em Spatial subdivisions} 
     974 
     975    uniform grid, hierarchical grid, kD-tree, BSP tree, octree, quadtree, etc. 
     976     
     977  \item {\em Bounding volume hierarchies} 
     978 
     979    hierarchy of bounding spheres, 
     980    hierarchy of bounding boxes, etc. 
     981     
     982  \item {\em Hybrid} 
     983 
     984    hierarchy of uniform grids, hierarchy of kD-trees, etc. 
     985 
     986  \end{itemize} 
     987   
     988\item Line space data structures 
     989  \begin{itemize} 
     990  \item {\em General} 
     991 
     992    regular grid, kD-tree, BSP tree, etc. 
     993  \end{itemize} 
     994   
     995\end{itemize} 
     996 
     997 
     998 
     999\subsubsection{Use of graphics hardware} 
     1000 
     1001 Visibility algorithms can be accelerated by exploiting dedicated 
     1002graphics hardware. The hardware implementation of the z-buffer 
     1003algorithm that is common even on a low-end graphics hardware can be 
     1004used to accelerate solutions to other visibility problems. Recall that the 
     1005z-buffer algorithm solves the visibility from a point problem by 
     1006providing a discrete approximation of the visible surfaces. 
     1007%$(3-D-2b(i), A-3b)$  
     1008 
     1009A visibility algorithm can be accelerated by the graphics hardware if 
     1010it can be decomposed so that the decomposition includes the 
     1011problem solved by the z-buffer or a series of such problems. 
     1012%$(3-D-2b(i), A-3b)$ 
     1013Prospectively, the recent features of the graphics hardware, such as 
     1014the pixel and vertex shaders  allow easier application of the graphics 
     1015hardware for solving specific visibility tasks. The software interface 
     1016between the graphics hardware and the CPU is usually provided by 
     1017OpenGL~\cite{Moller02-RTR}. 
     1018 
     1019 
     1020\section{Visibility in urban environments} 
     1021 
     1022 Urban environments constitute an important class of real world scenes 
     1023computer graphics deals with. We can identify two fundamental 
     1024subclasses of urban scenes. Firstly, we consider {\em outdoor} scenes, 
     1025i.e. urban scenes as observed from streets, parks, rivers, or a 
     1026bird's-eye view. Secondly, we consider {\em indoor} scenes, i.e. urban 
     1027scenes representing building interiors. In the following two sections 
     1028we discuss the essential characteristics of visibility in both the 
     1029outdoor and the indoor scenes. The discussion is followed by 
     1030summarizing the suitable visibility techniques. 
     1031 
     1032 
     1033\subsection{Analysis of visibility in outdoor urban areas} 
     1034 
     1035\label{sec:analysis_ue} 
     1036\label{sec:ANALYSIS_UE} 
     1037 
     1038 
     1039Outdoor urban scenes are viewed using two different scenarios. In a 
     1040{\em flyover} scenario the scene is observed from the bird's eye 
     1041view. A large part of the scene is visible. Visibility is  mainly 
     1042restricted due to the structure of the terrain, atmospheric 
     1043constraints (fog, clouds) and the finite resolution of human 
     1044retina. Rendering of the flyover scenarios is usually accelerated 
     1045using LOD, image-based rendering and terrain visibility algorithms, 
     1046but there is no significant potential for visibility culling. 
     1047 
     1048In a {\em walkthrough} scenario the scene is observed from a 
     1049pedestrians point of view and the visibility is often very 
     1050restricted. In the remainder of this section we discuss the walkthrough 
     1051scenario in more detail. 
     1052 
     1053Due to technological and physical restrictions urban scenes viewed 
     1054from outdoor closely resemble a 2D {\em height function}, i.e. a 
     1055function expressing the height of the scene elements above the ground. 
     1056The height function cannot capture certain objects such as bridges, 
     1057passages, subways, or detailed objects such as trees.  Nevertheless 
     1058buildings, usually the most important part of the scene, can be 
     1059captured accurately by the height function in most cases.  For the 
     1060sake of visibility computations the objects that cannot be represented 
     1061by the height function can be ignored. The resulting scene is then 
     1062called a {\em \m25d scene}. 
     1063 
     1064 In a dense urban area with high buildings visibility is very 
     1065restricted when the scene is viewed from a street (see 
     1066Figure~\ref{fig:outdoor}-a). Only buildings from nearby streets are 
     1067visible. Often there are no buildings visible above roofs of buildings 
     1068close to the viewpoint. In such a case visibility is essentially 
     1069two-dimensional, i.e. it could be solved accurately using a 2D 
     1070footprint of the scene and a 2D visibility algorithm. In areas with 
     1071smaller houses of different shapes visibility is not so severely 
     1072restricted since some objects can be visible by looking over other 
     1073objects. The view complexity increases (measured in number of visible 
     1074objects) and the height structure becomes increasingly 
     1075important. Complex views with far visibility can be seen also near 
     1076rivers, squares, and parks (see Figure~\ref{fig:outdoor}-b). 
     1077 
     1078\begin{figure}[htb] 
     1079  \centerline{ 
     1080    \hfill 
     1081    \includegraphics[width=0.45\textwidth,draft=\DRAFTIMAGES]{images/foto_street1} 
     1082    \hfill 
     1083    \includegraphics[width=0.45\textwidth,draft=\DRAFTIMAGES]{images/foto_castle1} 
     1084    \hfill 
     1085  } 
     1086  \caption{Visibility in outdoor urban areas. (left) In the center of a city 
     1087  visibility is typically restricted to a few nearby streets. (right) 
     1088  Near river banks typically a large part of the city is visible. Note 
     1089  that many distant objects are visible due to the terrain gradation.} 
     1090  \label{fig:outdoor} 
     1091\end{figure} 
     1092 
     1093 In scenes with large differences in terrain height the view complexity 
     1094is often very high. Many objects can be visible that are situated for 
     1095example on a hill or on a slope behind a river. Especially in areas 
     1096with smaller housing visibility is much defined by the terrain itself. 
     1097 
     1098We can summarize the observations as follows (based on 
     1099Wonka~\cite{wonka_phd}) : 
     1100 
     1101\begin{itemize} 
     1102   
     1103\item 
     1104  Outdoor urban environments have basically \m25d structure and 
     1105  consequently visibility is restricted accordingly. 
     1106   
     1107\item 
     1108  The view is very restricted in certain areas, such as in the 
     1109  city center. However the complexity of the view can vary 
     1110  significantly.  It is always not the case that only few objects are 
     1111  visible. 
     1112   
     1113\item 
     1114  If there are large height differences in the terrain, many 
     1115  objects are visible for most viewpoints. 
     1116   
     1117\item 
     1118  In the same view a close object can be visible next to a very 
     1119  distant one. 
     1120 
     1121\end{itemize} 
     1122 
     1123 
     1124 In the simplest case the outdoor scene consists only of the terrain 
     1125populated by a few buildings. Then the visibility can be calculated on 
     1126the terrain itself with satisfying 
     1127accuracy~\cite{Floriani:1995:HCH,Cohen-Or:1995:VDZ, Stewart:1997:HVT}. 
     1128Outdoor urban environments have a similar structure as terrains: 
     1129buildings can be treated as a terrain with {\em many discontinuities} 
     1130in the height function (assuming that the buildings do not contain 
     1131holes or significant variations in their fa{\c{c}}ades). To 
     1132accurately capture visibility in such an environment specialized 
     1133algorithms have been developed that compute visibility from a given 
     1134viewpoint~\cite{downs:2001:I3DG} or view 
     1135cell~\cite{wonka00,koltun01,bittner:2001:PG}. 
     1136 
     1137%  The methods presented later in the thesis make use of the specific 
     1138% structure of the outdoor scenes to efficiently compute a PVS for the 
     1139% given view cell.  The key observation is that the PVS for a view cell 
     1140% in a \m25d can be determined by computing visibility from its top 
     1141% boundary edges. This problem becomes a restricted variant of the 
     1142% visibility from a line segment in 3D with $d({\cal L}_R) = 3$. 
     1143 
     1144 
     1145\subsection{Analysis of indoor visibility} 
     1146 
     1147Building interiors constitute another important class of real world 
     1148scenes.  A typical building consists of rooms, halls, corridors, and 
     1149stairways. It is possible to see from one room to another through an 
     1150open door or window. Similarly it is possible to see from one corridor 
     1151to another one through a door or other connecting structure.  In 
     1152general the scene can be subdivided into cells corresponding to the 
     1153rooms, halls, corridors, etc., and transparent portals that connect 
     1154the cells~\cite{Airey90,Teller:1991:VPI}. Some portals 
     1155correspond to the real doors and windows, others provide only a 
     1156virtual connection between cells. For example an L-shaped corridor 
     1157can be represented by two cells and one virtual portal connecting them. 
     1158 
     1159Visibility in a building interior is often significantly restricted 
     1160(see Figure~\ref{fig:indoor}).  We can see the room we are located at 
     1161and possibly few other rooms visible through open doors. Due to the 
     1162natural partition of the scene into cells and portals visibility can 
     1163be solved by determining which cells can be seen through a give set of 
     1164portals and their sequences. A sequence of portals that we can see 
     1165through is called {\em feasible}. 
     1166 
     1167\begin{figure}[htb] 
     1168  \centerline{ 
     1169    \hfill 
     1170    \includegraphics[width=0.45\textwidth,draft=\DRAFTIMAGES]{images/foto_chodba1} 
     1171    \hfill 
     1172    \includegraphics[width=0.45\textwidth,draft=\DRAFTIMAGES]{images/foto_sloupy2} 
     1173    \hfill 
     1174  }  
     1175  \caption{Indoor visibility. (left) Visibility in indoor scenes is typically 
     1176    restricted to a few rooms or corridors. (right) In scenes with more complex 
     1177    interior structure visibility gets more complicated. 
     1178  } 
     1179  \label{fig:indoor} 
     1180\end{figure} 
     1181 
     1182 
     1183 Many algorithms for computing indoor visibility~\cite{Airey90, 
     1184Teller92phd, Luebke:1995:PMS} exploit the cell/portal structure of the 
     1185scene. The potential problem of this approach is its strong 
     1186sensitivity to the arrangement of the environment. In a scene with a 
     1187complicated structure with many portals there are many feasible portal 
     1188sequences.  Imagine a hall with columns arranged on a grid. The number 
     1189of feasible portal sequences rapidly increases with the distance from 
     1190the given view cell~\cite{Teller92phd} if the columns are sufficiently 
     1191small (see Figure~\ref{fig:portal_explosion}).  Paradoxically most of 
     1192the scene is visible and there is almost no benefit of using any 
     1193visibility culling algorithm. 
     1194 
     1195 The methods presented later in the report partially avoids this 
     1196problem since it does not rely on finding feasible portal sequences 
     1197even in the indoor scenes. Instead of determining what {\em can} be 
     1198visible through a transparent complement of the scene (portals) the 
     1199method determines what {\em cannot} be visible due to the scene 
     1200objects themselves (occluders). This approach also avoids the explicit 
     1201enumeration of portals and the construction of the cell/portal graph. 
     1202 
     1203 
     1204 
     1205\begin{figure}[htb]      
     1206  \centerline{ 
     1207    \includegraphics[width=0.45\textwidth,draft=\DRAFTFIGS]{figs/portals_explosion}} 
     1208    \caption{In sparsely occluded scenes the cell/portal algorithm can 
     1209    exhibit a combinatorial explosion in number of feasible portal 
     1210    sequences. Paradoxically visibility culling provides almost no 
     1211    benefit in such scenes.} 
     1212    \label{fig:portal_explosion} 
     1213\end{figure} 
     1214 
     1215 
     1216 
     1217\section{Summary} 
     1218 
     1219 Visibility problems and algorithms penetrate a large part of computer 
     1220graphics research. The proposed taxonomy aims to classify visibility 
     1221problems independently of their target application. The 
     1222classification should help to understand the nature of the given 
     1223problem and it should assist in finding relationships between 
     1224visibility problems and algorithms in different application areas. 
     1225The tools address the following classes of visibility problems: 
     1226 
     1227\begin{itemize} 
     1228\item Visibility from a point in 3D $d({\cal L}_R)=2$. 
     1229\item Global visibility in 3D $d({\cal L}_R)=4$. 
     1230\item Visibility from a region in 3D, $d({\cal L}_R)=4$. 
     1231\end{itemize} 
     1232 
     1233 This chapter discussed several important criteria for the 
     1234classification of visibility algorithms. This classification can be 
     1235seen as a finer structuring of the taxonomy of visibility problems. We 
     1236discussed important steps in the design of a visibility algorithm that 
     1237should also assist in understanding the quality of a visibility 
     1238algorithm. According to the classification the tools address 
     1239algorithms with the following properties: 
     1240 
     1241\begin{itemize} 
     1242\item Domain: 
     1243  \begin{itemize} 
     1244  \item viewpoint (Chapter~\ref{chap:online}), 
     1245  \item polygon or polyhedron (Chapters~\ref{chap:sampling,chap:mutual}) 
     1246  \end{itemize} 
     1247\item Scene restrictions (occluders): 
     1248  \begin{itemize} 
     1249  \item meshes consisting of convex polygons 
     1250  \end{itemize} 
     1251\item Scene restrictions (group objects): 
     1252  \begin{itemize} 
     1253  \item bounding boxes (Chapters~\ref{chap:rtviscull},~\ref{chap:vismap},~Chapter~\ref{chap:rot25d} and~\ref{chap:rot3d}), 
     1254  \end{itemize} 
     1255\item Output: 
     1256  \begin{itemize} 
     1257  \item Visibility classification of objects or hierarchy nodes 
     1258  \item PVS 
     1259  \end{itemize} 
     1260\item Accuracy: 
     1261  \begin{itemize} 
     1262  \item conservative 
     1263  \item exact 
     1264  \item aggresive 
     1265  \end{itemize} 
     1266  \item Solution space: 
     1267  \begin{itemize} 
     1268  \item discrete (Chapters~\ref{chap:online},~\ref{chap:sampling}) 
     1269  \item continuous, line space / primal space (Chapter~\ref{chap:rot25d}) 
     1270  \end{itemize} 
     1271  \item Solution space data structures: viewport (Chapter~\ref{chap:online}), ray stack (Chapter~\ref{chap:sampling}), ray stack or BSP tree (Chapter~\ref{chap:mutual}) 
     1272  \item Use of coherence of visibility: 
     1273    \begin{itemize} 
     1274    \item spatial coherence (all methods) 
     1275    \item temporal coherence (Chapter~\ref{chap:online}) 
     1276    \end{itemize} 
     1277  \item Output sensitivity: expected in practice (all methods) 
     1278  \item Acceleration data structure: kD-tree (all methods) 
     1279  \item Use of graphics hardware: Chapter~\ref{chap:online} 
     1280     
     1281  \end{itemize} 
     1282 
     1283 

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    r251 r255  
    129129\end{itemize} 
    130130 
    131 We meet these requirements by using a view cell BSP tree, where the BSP leafs are associated with the view cells.  
    132 Using the BSP tree, we are able to find the initial view cells with only a few view ray-plane intersections.  
    133 The hierarchical structure of the BSP tree can be exploited as hierarchy of view cells. If neccessary, the BSP  
    134 approach makes it very easy to further subdivide a view cell. 
     131We meet these requirements by using a view cell BSP tree, where the 
     132BSP leafs are associated with the view cells.  Using the BSP tree, we 
     133are able to find the initial view cells with only a few view ray-plane 
     134intersections.  The hierarchical structure of the BSP tree can be 
     135exploited as hierarchy of view cells. If neccessary, the BSP  approach 
     136makes it very easy to further subdivide a view cell. 
    135137 
    136138Currently we use two approaches to generate the initial BSP view cell tree. 
    137139 
    138140\begin{itemize} 
    139 \item We use a number of dedicated input view cells. As input view cell any closed mesh can be applied. The only requirement 
    140 is that the view cells do not overlap. We insert one view cell after the other into the tree. The polygons of a view cell are filtered down the tree, guiding the insertion process. Once we reach a leaf and there are no more polygons left, we terminate 
    141 the tree subdivision. If we are on the inside of the last split plane (i.e., the leaf is representing the inside of the view cell), we associate the leaf with the view cell (i.e., add a pointer to the view cell). Hence a number of leafes 
    142 can be associated with the same input view cell. 
    143 \item We apply the BSP tree subdivision to the scene geometry. When the subdivision terminates, the leaf nodes 
    144 also represent the view cells.  
     141\item We use a number of dedicated input view cells. As input view 
     142cell any closed mesh can be applied. The only requirement is that the 
     143view cells do not overlap. We insert one view cell after the other 
     144into the tree. The polygons of a view cell are filtered down the tree, 
     145guiding the insertion process. Once we reach a leaf and there are no 
     146more polygons left, we terminate the tree subdivision. If we are on 
     147the inside of the last split plane (i.e., the leaf is representing the 
     148inside of the view cell), we associate the leaf with the view cell 
     149(i.e., add a pointer to the view cell). Hence a number of leafes can 
     150be associated with the same input view cell. 
     151\item We apply the BSP tree subdivision to the scene geometry. When 
     152the subdivision terminates, the leaf nodes also represent the view 
     153cells. 
    145154\end{itemize} 
    146155 

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    r251 r255  
    77The proposed visibility preprocessing framework consists of two major 
    88steps. 
     9 
    910\begin{itemize} 
    1011\item The first step is an aggresive visibility sampling which gives 
    … …  
    1314section~\ref{sec:sampling}. The imporant property of the aggresive 
    1415sampling step is that it provides a fast progressive solution to 
    15 global visibility and thus it can be easily integrated into the 
    16 game development cycle. 
    17  
    18 \item The second step is visibility verification. This step turns the 
    19 previous aggresive visibility solution into either exact, conservative 
    20 or error bound aggresive solution. The choice of the particular 
    21 verifier is left on the user in order to select the best for a 
    22 particular scene, application context and time constrains. For 
     16global visibility and thus it can be easily integrated into the game 
     17development cycle. 
     18 
     19\item The second step is mutual visibility verification. This step 
     20turns the previous aggresive visibility solution into either exact, 
     21conservative or error bound aggresive solution. The choice of the 
     22particular verifier is left on the user in order to select the best 
     23for a particular scene, application context and time constrains. For 
    2324example, in scenes like a forest an error bound aggresive visibility 
    2425can be the best compromise between the resulting size of the PVS (and 
    2526framerate) and the visual quality. The exact or conservative algorithm 
    2627can however be chosen for urban scenes where of even small objects can 
    27 be more distructing for the user. 
     28be more distructing for the user. The mutual visibility tool will be 
     29described in the next chapter. 
     30 
    2831\end{itemize} 
    29  
    3032 
    3133 
    … …  
    4345\end{itemize} 
    4446 
    45 Both of these points are addressed bellow in more detail. 
     47Both these points will be addressed in this chapter in more detail. 
     48 
     49 
     50 
     51\section{Related work} 
     52\label{VFR3D_RELATED_WORK} 
     53 
     54 
     55 Below we briefly discuss the related work on visibility preprocessing 
     56in several application areas. In particular we focus on computing 
     57from-region which has been a core of most previous visibility 
     58preprocessing techniques. 
     59 
     60 
     61\subsection{Aspect graph} 
     62 
     63The first algorithms dealing with from-region visibility belong to the 
     64area of computer vision. The {\em aspect 
     65graph}~\cite{Gigus90,Plantinga:1990:RTH, Sojka:1995:AGT} partitions 
     66the view space into cells that group viewpoints from which the 
     67projection of the scene is qualitatively equivalent. The aspect graph 
     68is a graph describing the view of the scene (aspect) for each cell of 
     69the partitioning. The major drawback of this approach is that for 
     70polygonal scenes with $n$ polygons there can be $\Theta(n^9)$ cells in 
     71the partitioning for unrestricted viewspace. A {\em scale space} 
     72aspect graph~\cite{bb12595,bb12590} improves robustness of the method 
     73by merging similar features according to the given scale. 
     74 
     75 
     76\subsection{Potentially visible sets} 
     77 
     78 
     79 In the computer graphics community Airey~\cite{Airey90} introduced 
     80the concept of {\em potentially visible sets} (PVS).  Airey assumes 
     81the existence of a natural subdivision of the environment into 
     82cells. For models of building interiors these cells roughly correspond 
     83to rooms and corridors.  For each cell the PVS is formed by cells 
     84visible from any point of that cell.  Airey uses ray shooting to 
     85approximate visibility between cells of the subdivision and so the 
     86computed PVS is not conservative. 
     87 
     88This concept was further elaborated by Teller et 
     89al.~\cite{Teller92phd,Teller:1991:VPI} to establish a conservative 
     90PVS.  The PVS is constructed by testing the existence of a stabbing 
     91line through a sequence of polygonal portals between cells. Teller 
     92proposed an exact solution to this problem using \plucker 
     93coordinates~\cite{Teller:1992:CAA} and a simpler and more robust 
     94conservative solution~\cite{Teller92phd}.  The portal based methods 
     95are well suited to static densely occluded environments with a 
     96particular structure.  For less structured models they can face a 
     97combinatorial explosion of complexity~\cite{Teller92phd}. Yagel and 
     98Ray~\cite{Yagel95a} present an algorithm, that uses a regular spatial 
     99subdivision. Their approach is not sensitive to the structure of the 
     100model in terms of complexity, but its efficiency is altered by the 
     101discrete representation of the scene. 
     102 
     103Plantinga proposed a PVS algorithm based on a conservative viewspace 
     104partitioning by evaluating visual 
     105events~\cite{Plantinga:1993:CVP}. The construction of viewspace 
     106partitioning was further studied by Chrysanthou et 
     107al.~\cite{Chrysanthou:1998:VP}, Cohen-Or et al.~\cite{cohen-egc-98} 
     108and Sadagic~\cite{Sadagic}.  Sudarsky and 
     109Gotsman~\cite{Sudarsky:1996:OVA} proposed an output-sensitive 
     110visibility algorithm for dynamic scenes.  Cohen-Or et 
     111al.~\cite{COZ-gi98} developed a conservative algorithm determining 
     112visibility of an $\epsilon$-neighborhood of a given viewpoint that was 
     113used for network based walkthroughs. 
     114 
     115Conservative algorithms for computing PVS developed by Durand et 
     116al.~\cite{EVL-2000-60} and Schaufler et al.~\cite{EVL-2000-59}  make 
     117use of several simplifying assumptions to avoid the usage of 4D data 
     118structures.  Wang et al.~\cite{Wang98} proposed an algorithm that 
     119precomputes visibility within beams originating from the restricted 
     120viewpoint region. The approach is very similar to the 5D subdivision 
     121for ray tracing~\cite{Simiakakis:1994:FAS} and so it exhibits similar 
     122problems, namely inadequate memory and preprocessing complexities. 
     123Specialized algorithms for computing PVS in \m25d scenes were proposed 
     124by Wonka et al.~\cite{wonka00}, Koltun et al.~\cite{koltun01}, and 
     125Bittner et al.~\cite{bittner:2001:PG}. 
     126 
     127The exact mutual visibility method presented later in the report is 
     128based on method exploting \plucker coordinates of 
     129lines~\cite{bittner02phd,nirenstein:02:egwr,haumont2005}. This 
     130algorithm uses \plucker coordinates to compute visibility in shafts 
     131defined by each polygon in the scene. 
     132 
     133 
     134\subsection{Rendering of shadows} 
     135 
     136 
     137The from-region visibility problems include the computation of soft 
     138shadows due to an areal light source. Continuous algorithms for 
     139real-time soft shadow generation were studied by Chin and 
     140Feiner~\cite{Chin:1992:FOP}, Loscos and 
     141Drettakis~\cite{Loscos:1997:IHS}, and 
     142Chrysanthou~\cite{Chrysantho1996a} and Chrysanthou and 
     143Slater~\cite{Chrysanthou:1997:IUS}. Discrete solutions have been 
     144proposed by Nishita~\cite{Nishita85}, Brotman and 
     145Badler~\cite{Brotman:1984:GSS}, and Soler and Sillion~\cite{SS98}. An 
     146exact algorithm computing an antipenumbra of an areal light source was 
     147developed by Teller~\cite{Teller:1992:CAA}. 
     148 
     149 
     150\subsection{Discontinuity meshing} 
     151 
     152 
     153Discontinuity meshing is used in the context of the radiosity global 
     154illumination algorithm or computing soft shadows due to areal light 
     155sources.  First approximate discontinuity meshing algorithms were 
     156studied by Campbell~\cite{Campbell:1990:AMG, Campbell91}, 
     157Lischinski~\cite{lischinski92a}, and Heckbert~\cite{Heckbert92discon}. 
     158More elaborate methods were developed by 
     159Drettakis~\cite{Drettakis94-SSRII, Drettakis94-FSAAL}, and Stewart and 
     160Ghali~\cite{Stewart93-OSACS, Stewart:1994:FCSb}. These methods are 
     161capable of creating a complete discontinuity mesh that encodes all 
     162visual events involving the light source. 
     163 
     164The classical radiosity is based on an evaluation of form factors 
     165between two patches~\cite{Schroder:1993:FFB}. The visibility 
     166computation is a crucial step in the form factor 
     167evaluation~\cite{Teller:1993:GVA,Haines94,Teller:1994:POL, 
     168Nechvile:1996:FFE,Teichmann:WV}. Similar visibility computation takes 
     169place in the scope of hierarchical radiosity 
     170algorithms~\cite{Soler:1996:AEB, Drettakis:1997:IUG, Daubert:1997:HLS}. 
     171 
     172 
     173 
     174\subsection{Global visibility} 
     175 
     176 The aim of {\em global visibility} computations is to capture and 
     177describe visibility in the whole scene~\cite{Durand:1996:VCN}. The 
     178global visibility algorithms are typically based on some form of {\em 
     179line space subdivision} that partitions lines or rays into equivalence 
     180classes according to their visibility classification. Each class 
     181corresponds to a continuous set of rays with a common visibility 
     182classification. The techniques differ mainly in the way how the line 
     183space subdivision is computed and maintained. A practical application 
     184of most of the proposed global visibility structures for 3D scenes is 
     185still an open problem.  Prospectively these techniques provide an 
     186elegant method for ray shooting acceleration --- the ray shooting 
     187problem can be reduced to a point location in the line space 
     188subdivision. 
     189 
     190 
     191Pocchiola and Vegter introduced the visibility complex~\cite{pv-vc-93} 
     192that describes global visibility in 2D scenes. The visibility complex 
     193has been applied to solve various 2D visibility 
     194problems~\cite{r-tsvcp-95,r-wvcav-97, r-dvpsv-97,Orti96-UVCRC}.  The 
     195approach was generalized to 3D by Durand et 
     196al.~\cite{Durand:1996:VCN}. Nevertheless, no implementation of the 3D 
     197visibility complex is currently known. Durand et 
     198al.~\cite{Durand:1997:VSP} introduced the {\em visibility skeleton} 
     199that is a graph describing a skeleton of the 3D visibility 
     200complex. The visibility skeleton was verified experimentally and  the 
     201results indicate that its $O(n^4\log n)$ worst case complexity is much 
     202better in practice. Pu~\cite{Pu98-DSGIV} developed a similar method to 
     203the one presented in this chapter. He uses a BSP tree in \plucker 
     204coordinates to represent a global visibility map for a given set of 
     205polygons. The computation is performed considering all rays piercing 
     206the scene and so the method exhibits unacceptable memory complexity 
     207even for scenes of moderate size. Recently, Duguet and 
     208Drettakis~\cite{duguet:02:sig} developed a robust variant of the 
     209visibility skeleton algorithm that uses robust epsilon-visibility 
     210predicates. 
     211 
     212 Discrete methods aiming to describe visibility in a 4D data structure 
     213were presented by Chrysanthou et al.~\cite{chrysanthou:cgi:98} and 
     214Blais and Poulin~\cite{blais98a}.  These data structures are closely 
     215related to the {\em lumigraph}~\cite{Gortler:1996:L,buehler2001} or 
     216{\em light field}~\cite{Levoy:1996:LFR}. An interesting discrete 
     217hierarchical visibility algorithm for two-dimensional scenes was 
     218developed by Hinkenjann and M\"uller~\cite{EVL-1996-10}. One of the 
     219biggest problems of the discrete solution space data structures is 
     220their memory consumption required to achieve a reasonable 
     221accuracy. Prospectively, the scene complexity 
     222measures~\cite{Cazals:3204:1997} provide a useful estimate on the 
     223required sampling density and the size of the solution space data 
     224structure. 
     225 
     226 
     227\subsection{Other applications} 
     228 
     229 Certain from-point visibility problems determining visibility over a 
     230period of time can be transformed to a static from-region visibility 
     231problem. Such a transformation is particularly useful for antialiasing 
     232purposes~\cite{grant85a}. The from-region visibility can also be used 
     233in the context of simulation of the sound 
     234propagation~\cite{Funkhouser98}. The sound propagation algorithms 
     235typically require lower resolution than the algorithms simulating the 
     236propagation of light, but they need to account for simulation of 
     237attenuation, reflection and time delays. 
     238 
     239\section{Algorithm Setup} 
     240 
     241\subsection{View Cell Representation} 
     242 
     243In order to efficiently use view cells with our sampling method, we 
     244require a view cell representation which is 
     245 
     246\begin{itemize} 
     247\item optimized for viewcell - ray intersection. 
     248\item flexible, i.e., it can represent arbitrary geometry. 
     249\item naturally suited for an hierarchical approach. %(i.e., there is a root view cell containing all others) 
     250\end{itemize} 
     251 
     252We meet these requirements by using a view cell BSP tree, where the 
     253BSP leafs are associated with the view cells.  Using the BSP tree, we 
     254are able to find the initial view cells with only a few view ray-plane 
     255intersections.  The hierarchical structure of the BSP tree can be 
     256exploited as hierarchy of view cells. If neccessary, we could further 
     257subdivide a BSP leaf view cell quite easily. 
     258 
     259Currently we use two approaches to generate the initial BSP view cell tree. 
     260 
     261\begin{itemize} 
     262\item We use a number of dedicated input view cells. As input view 
     263cell any closed mesh can be applied. The only requirement is that the 
     264view cells do not overlap. We insert one view cell after the other 
     265into the tree. The polygons of a view cell are filtered down the tree, 
     266guiding the insertion process. Once we reach a leaf and there are no 
     267more polygons left, we terminate the tree subdivision. If we are on 
     268the inside of the last split plane (i.e., the leaf is representing the 
     269inside of the view cell), we associate the leaf with the view cell 
     270(i.e., add a pointer to the view cell). Hence a number of leafes can 
     271be associated with the same input view cell. 
     272\item We apply the BSP tree subdivision to the scene geometry. When 
     273the subdivision terminates, the leaf nodes also represent the view 
     274cells. 
     275\end{itemize} 
    46276 
    47277\subsection{From-object based visibility} 
    … …  
    74304 
    75305 
    76 \subsection{Basic Randomized Sampling} 
     306\section{Basic Randomized Sampling} 
    77307 
    78308 
    … …  
    97327 
    98328 
    99 \subsection{Accounting for View Cell Distribution} 
     329\section{Accounting for View Cell Distribution} 
    100330 
    101331The first modification to the basic algorithm accounts for irregular 
    … …  
    112342 
    113343 
    114 \subsection{Accounting for Visibility Events} 
     344\section{Accounting for Visibility Events} 
    115345 
    116346 Visibility events correspond to appearance and disapearance of 
    … …  
    125355 objects. 
    126356 
    127  \subsection{View Cell Representation} 
    128  
    129 In order to efficiently use view cells with our sampling method, we require a view cell representation which is 
    130  
    131 \begin{itemize} 
    132 \item optimized for viewcell - ray intersection. 
    133 \item flexible, i.e., it can represent arbitrary geometry. 
    134 \item naturally suited for an hierarchical approach. %(i.e., there is a root view cell containing all others) 
    135 \end{itemize} 
    136  
    137 We meet these requirements by using a view cell BSP tree, where the BSP leafs are associated with the view cells.  
    138 Using the BSP tree, we are able to find the initial view cells with only a few view ray-plane intersections.  
    139 The hierarchical structure of the BSP tree can be exploited as hierarchy of view cells. If neccessary, we could further subdivide a BSP leaf view cell quite easily. 
    140  
    141 Currently we use two approaches to generate the initial BSP view cell tree. 
    142  
    143 \begin{itemize} 
    144 \item We use a number of dedicated input view cells. As input view cell any closed mesh can be applied. The only requirement 
    145 is that the view cells do not overlap. We insert one view cell after the other into the tree. The polygons of a view cell are filtered down the tree, guiding the insertion process. Once we reach a leaf and there are no more polygons left, we terminate 
    146 the tree subdivision. If we are on the inside of the last split plane (i.e., the leaf is representing the inside of the view cell), we associate the leaf with the view cell (i.e., add a pointer to the view cell). Hence a number of leafes 
    147 can be associated with the same input view cell. 
    148 \item We apply the BSP tree subdivision to the scene geometry. When the subdivision terminates, the leaf nodes 
    149 also represent the view cells.  
    150 \end{itemize} 
    151  
     357 
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