Studies in Computational Intelligence

By: Dimitri Plemenos, Georgios Miaoulis

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Hardcover | 9 July 2009

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Hardcover
260 Pages

Dimensions(cm)
23.5 x 15.88 x 1.27

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In this book, three main notions will be used in the editors search of improvements in various areas of computer graphics: Artificial Intelligence, Viewpoint Complexity and Human Intelligence. Several Artificial Intelligence techniques are used in presented intelligent scene modelers, mainly declarative ones. Among them, the mostly used techniques are Expert systems, Constraint Satisfaction Problem resolution and Machine-learning. The notion of viewpoint complexity, that is complexity of a scene seen from a given viewpoint, will be used in improvement proposals for a lot of computer graphics problems like scene understanding, virtual world exploration, image-based modeling and rendering, ray tracing and radiosity. Very often, viewpoint complexity is used in conjunction with Artificial Intelligence techniques like Heuristic search and Problem resolution. The notions of artificial Intelligence and Viewpoint Complexity may help to automatically resolve a big number of computer graphics problems. However, there are special situations where is required to find a particular solution for each situation. In such a case, human intelligence has to replace, or to be combined with, artificial intelligence. Such cases, and proposed solutions are also presented in this book.

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Tools for Intelligent Computer Graphics	p. 1
Introduction	p. 1
Elements of Artificial Intelligence for Computer Graphics	p. 3
Problem resolution	p. 5
General Considerations	p. 5
Strategy games	p. 5
Constraint satisfaction	p. 6
The Resolution Process	p. 7
Constraint Logic Programming on Finite Domains -CLP(FD)	p. 8
Expert Systems	p. 9
Rules and inference engine of expert systems	p. 10
Main strategies for applying rules	p. 11
Machine-learning	p. 11
Neural networks	p. 12
Genetic algorithms	p. 14
Artificial Intelligence and Computer Graphics	p. 15
What is viewpoint complexity in Computer Graphics?	p. 17
How to compute visual complexity?	p. 18
Accurate visual complexity estimation	p. 19
Fast approximate estimation of visual complexity	p. 22
Conclusion	p. 23
References	p. 24
Intelligent scene modeling	p. 27
Introduction	p. 27
Declarative scene modeling	p. 28
The PolyFormes Declarative Scene Modeler	p. 29
The MultiFormes Declarative Scene Modeler	p. 29
The DE2MONS Declarative Scene Modeler	p. 30
The VoluFormes Declarative Scene Modeler	p. 30
The description phase in declarative scene modeling	p. 31
Set of rules and facts	p. 31
Set of arithmetic or geometric constraints	p. 32
The generation phase in declarative scene modeling	p. 34
Set of rules and facts	p. 34
Set of arithmetic or geometric constraints	p. 41
Further generation improvements	p. 49
Neural network-based machine-learning	p. 50
Genetic algorithm-based machine-learning	p. 56
The scene understanding phase in declarative scene modeling	p. 59
Conclusion	p. 61
References	p. 62
Scene understanding	p. 65
Introduction	p. 65
Static scene understanding	p. 66
Non degenerated views	p. 67
Direct approximate viewpoint calculation	p. 68
Iterative viewpoint calculation	p. 70
The viewpoint quality criterion	p. 74
Fast viewpoint quality calculation	p. 75
Information theory-based viewpoint calculation	p. 76
Total curvature-based viewpoint calculation	p. 78
Mesh Saliency-based viewpoint calculation	p. 81
Object understanding-based viewpoint calculation	p. 82
A priori viewpoint calculation	p. 85
Processing of form properties	p. 88
Processing of placement properties	p. 89
Conclusion	p. 92
References	p. 93
Virtual World Exploration	p. 95
Introduction	p. 95
Global incremental online exploration	p. 97
A heuristic method to guide the movement of a camera	p. 99
Fast computation of good view directions	p. 101
Camera movement heuristics	p. 103
Viewpoint entropy-based online exploration	p. 106
Goal-based global online exploration	p. 108
Main principles of goal-based exploration	p. 108
Implementation of exploration techniques and results	p. 1ll
Discussion	p. 115
Local incremental online exploration	p. 117
Preprocessing: analysis of the scene	p. 118
Determining the trajectory of the camera	p. 120
Processing of a point of passage	p. 122
Discussion	p. 122
Minimal viewpoint set-based global offline Exploration	p. 124
Computing an initial set of points of view	p. 124
Total curvature-based global offline exploration	p. 139
Offline construction of a visibility graph	p. 139
Incremental computation of the camera trajectory	p. 140
Further exploration of a virtual world	p. 142
Some results	p. 142
Local offline exploration	p. 144
Construction of the visibility graph	p. 144
Computation of pertinent views	p. 145
Computation of camera trajectory	p. 146
Improvements	p. 148
Conclusion	p. 151
References	p. 152
Improvements and applications	p. 155
Introduction	p. 155
Scene understanding and lighting	p. 157
Absolute light source placement	p. 158
Taking into account light source position	p. 159
A naive first approach	p. 160
Refining the method	p. 162
Indirect lighting	p. 163
Automatic computation of the best viewpoint	p. 164
Automatic scene exploration	p. 164
First results	p. 165
Discussion	p. 168
Viewpoint complexity and image-based modeling	p. 169
Computing a minimal set of viewpoints - First method	p. 169
Computing a minimal set of viewpoints - Second method	p. 170
Evaluating camera positions	p. 171
Computing a minimal set of camera positions	p. 171
Some results	p. 172
Viewpoint complexity and ray-tracing	p. 172
Viewpoint complexity and molecular visualization	p. 173
Conclusion	p. 175
References	p. 176
Viewpoint Complexity in Radiosity	p. 179
Introduction	p. 179
Radiosity and form factors	p. 180
The Hemi-cube Method for Computing Form Factors	p. 181
The hemi-cube selective refinement technique	p. 182
Selective Refinement	p. 182
Selective Refinement for Computing Form Factors	p. 183
Hemisphere subdivision techniques	p. 187
Progressive refinement and Monte Carlo-based techniques	p. 187
Progressive Radiosity	p. 187
Monte-Carlo Radiosity	p. 188
Monte-Carlo Progressive Radiosity	p. 188
The first hemisphere subdivision technique	p. 189
Evaluation of the first hemisphere subdivision method	p. 192
A new approach of hemisphere subdivision	p. 194
Preprocessing	p. 194
Radiosity computation	p. 196
Discussion and results	p. 198
Conclusion	p. 201
Pyramidal subdivision	p. 202
Pyramidal hemisphere subdivision	p. 202
Adaptive Pyramidal Subdivision	p. 203
Regular Pyramidal Subdivision	p. 205
Number of Objects Contained Into a Pyramid	p. 207
Discussion and Results	p. 209
How to improve viewpoint complexity estimation	p. 211
More accurate Monte Carlo radiosity	p. 212
Subdivision depending on the neighboring regions	p. 213
Preprocessing	p. 213
Radiosity computation	p. 214
Independent processing of regions	p. 214
Preprocessing	p. 215
Radiosity computation	p. 216
Discussion	p. 216
Conclusion	p. 216
References	p. 219
Scene understanding and human intelligence	p. 221
Introduction	p. 221
Main techniques for scene understanding	p. 223
Best view computing for virtual worlds	p. 223
Apparent contour extraction techniques	p. 226
Image space algorithms	p. 227
Hybrid algorithms	p. 227
Object space algorithms	p. 227
Understanding visually complex scenes	p. 230
The selective refinement part	p. 231
The code direction part	p. 232
First results	p. 234
Discussion	p. 237
Understanding scenes with hidden details	p. 238
Using different rendering modes	p. 239
Creating holes on the external surfaces of the scene	p. 239
Using a preprocessing step	p. 240
Suppression of useless polygons during rendering	p. 240
Some results	p. 240
Discussion	p. 244
References	p. 245
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Studies in Computational Intelligence

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