Computer Vision and Computer Graphics

Theory and Applications - International Conference, VISIGRAPP 2008, Funchal-Madeira, Portugal, January 22-25, 2008. Revised Selected Papers
1. Auflage 2009
ISBN: 978-3-642-10226-4
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 290 Seiten

This book constitutes the refereed proceedings of the International Conference, VISIGRAPP 2008, the Joint Conference on Computer Vision Theory and Applications (VISAPP) and Computer Graphics Theory and Applications (GRAPP), held in Funchal-Madeira, Portugal, in January 2008. The 20 revised full papers presented were carefully reviewed and selected from a total of 374 submissions. The papers are organized in topical sections on geometry and modeling, rendering, interactive environments, image formation and processing, image analysis, image understanding, as well as motion, tracking and stereo vision.

Autoren/Hrsg.

Inhaltsverzeichnis

1;Preface;5
2;Organization;6
3;Table of Contents;12
4;Invited Papers;12
4.1;Virtual Reality: A Knowledge Tool for Cultural Heritage;15
4.1.1;Introduction;15
4.1.2;VR and Cultural Heritage Documentation;16
4.1.2.1;Suchilquitongo;17
4.1.3;Digital Reconstruction;17
4.1.3.1;Cacaxtla;17
4.1.4;Techniques Employed;18
4.1.4.1;The Registry;18
4.1.4.2;The Making of the 3D Models;20
4.1.5;Describing IXTLI;21
4.1.6;Results;22
4.1.7;Further Works;23
4.1.8;Conclusions;24
4.1.9;References;24
5;Computer Graphics Theory and Applications;12
5.1;Part I: Geometry and Modeling;12
5.1.1;Using Distance Information for Silhouette Preservation in Mesh Simplification Techniques;25
5.1.1.1;Introduction;25
5.1.1.2;Previous Work;26
5.1.1.2.1;Mesh Simplification;26
5.1.1.2.2;Digital Distance Transforms;27
5.1.1.3;Method Description;27
5.1.1.3.1;View-Dependent Distance Labels Computation;28
5.1.1.3.2;Distance Labels Interpolation for New Points of View;31
5.1.1.3.3;Mesh Simplification;31
5.1.1.4;Results;33
5.1.1.5;Conclusions and Future Work;37
5.1.1.6;References;38
5.1.2;Closed-Form Solutions for Continuous PCA and Bounding Box Algorithms;40
5.1.2.1;Introduction;40
5.1.2.2;PCA;42
5.1.2.2.1;Continuous PCA;43
5.1.2.3;Evaluation of the Expressions for Continuous PCA;44
5.1.2.3.1;Continuous PCA in $R^2$;44
5.1.2.3.2;Continuous PCA in $R^3$;46
5.1.2.4;Experimental Results;48
5.1.2.4.1;Evaluation of the PCA and CPCA Bounding Box Algorithms;49
5.1.2.4.2;Evaluation of Other Bounding Box Algorithms;52
5.1.2.5;Conclusions;53
5.1.2.6;References;54
5.2;Part II: Rendering;12
5.2.1;An Importance Sampling Method for Arbitrary BRDFs;55
5.2.1.1;Introduction;55
5.2.1.2;Reflectance Equation and Monte-Carlo Estimation;56
5.2.1.2.1;MC Numerical Estimation of $L_r$;56
5.2.1.2.2;Sampling the BRDF;57
5.2.1.3;Our Algorithm;59
5.2.1.3.1;Building the Adaptive Structures;59
5.2.1.3.2;Obtaining Sample Directions;60
5.2.1.3.3;Quadtree Traversing for Optimal Sampling;62
5.2.1.3.4;Quadtree Set Construction Requirements;62
5.2.1.4;Results;63
5.2.1.4.1;Sampling Analytical BRDF Models;63
5.2.1.4.2;Adaptive Sampling of Measured Data;65
5.2.1.5;Conclusions;66
5.2.1.6;References;67
5.2.2;Generalization of Single-Center Projections Using Projection Tile Screens;69
5.2.2.1;Introduction;69
5.2.2.2;Related Work;71
5.2.2.3;Real-Time Non-planar Projections;71
5.2.2.3.1;Projection Functions;73
5.2.2.3.2;Adjusting the Orientation of the Projection Camera;73
5.2.2.3.3;Normal-Map Optimization;74
5.2.2.4;Concept of Projection Tiles;75
5.2.2.4.1;Tile Features;76
5.2.2.4.2;Projection Tiles and Projection Tile Screens;76
5.2.2.4.3;Mapping Projection-Tile-Screens to Normal-Maps;77
5.2.2.5;Implementation Details;77
5.2.2.5.1;Cube Map Creation;78
5.2.2.5.2;Applying Projections;79
5.2.2.6;Experimental Results;79
5.2.2.6.1;Application Examples;79
5.2.2.6.2;Problems and Limitations;81
5.2.2.7;Conclusions and Outlook;81
5.2.2.8;References;81
5.3;Part III: Interactive Environments;12
5.3.1;Real-Time Generation of Interactive Virtual Human Behaviours;84
5.3.1.1;Introduction;84
5.3.1.2;Model of Interactive Behaviours;85
5.3.1.3;Generating Interactive Behaviours;86
5.3.1.3.1;The Windowed Viterbi Algorithm;86
5.3.1.3.2;Estimating Output Behaviours;87
5.3.1.3.3;Improving the Model for Tracking Accuracy;89
5.3.1.4;Results;90
5.3.1.4.1;Assessing the Accuracy of the Generated Behaviour;91
5.3.1.4.2;Visual Evaluation;93
5.3.1.4.3;Assessing the Accuracy of the Tracking Results;95
5.3.1.5;Conclusions;95
5.3.1.6;References;96
5.3.2;CoGenIVE: Building 3D Virtual Environments Using a Model Based User Interface Design Approach;97
5.3.2.1;Introduction;97
5.3.2.2;Related Work;98
5.3.2.3;The VR-DeMo Process;99
5.3.2.4;Dialog Model;100
5.3.2.4.1;Defining the States;100
5.3.2.4.2;Handling Input;101
5.3.2.5;Presentation Model;102
5.3.2.6;Interaction Description;103
5.3.2.6.1;NiMMiT Basic Primitives;104
5.3.2.6.2;Additional Features;104
5.3.2.6.3;Tool Support;105
5.3.2.7;Application Prototypes;106
5.3.2.8;Practical Use of CoGenIVE;107
5.3.2.9;Conclusions;108
5.3.2.10;References;108
6;Computer Vision Theory and Applications;12
6.1;Part I: Image Formation and Processing;12
6.1.1;Fast Medial Axis Extraction Algorithm on Tubular Large 3D Data by Randomized Erosion;111
6.1.1.1;Introduction;111
6.1.1.2;Basic Notions;112
6.1.1.2.1;Morphological Operators on Binary Images;112
6.1.1.2.2;Accelerated Thinning and Skeleton Definition;113
6.1.1.3;Methods;114
6.1.1.3.1;Skeletonization Method;114
6.1.1.3.2;Algorithm Details;114
6.1.1.4;Results;117
6.1.1.4.1;Fast Morphologic Operators on Binary Images;117
6.1.1.4.2;Fast Skeletonization Algorithm;117
6.1.1.5;Conclusions;121
6.1.1.6;References;122
6.1.2;Self-calibration of Central Cameras from Point Correspondences by Minimizing Angular Error;123
6.1.2.1;Introduction;123
6.1.2.2;Central Camera Models;124
6.1.2.2.1;Image Formation in Central Cameras;124
6.1.2.2.2;Radial Projection Models;126
6.1.2.2.3;Backward Models;126
6.1.2.3;Self-calibration Method;127
6.1.2.3.1;Minimization of Angular Error for Two Views;127
6.1.2.3.2;Constraints on Camera Parameters;129
6.1.2.3.3;Robustness for Outliers;129
6.1.2.3.4;Three Views;129
6.1.2.4;Experiments;130
6.1.2.4.1;Synthetic Data;130
6.1.2.4.2;Real Data;133
6.1.2.5;Discussion;134
6.1.2.6;Conclusions;135
6.1.2.7;References;135
6.1.3;Image Filtering Based on Locally Estimated Geodesic Functions;137
6.1.3.1;Introduction;137
6.1.3.2;Similarity Measure Based on Geodesic Time;139
6.1.3.2.1;Geodesic Time on Greylevel Images;139
6.1.3.2.2;A New Geodesic Similarity Measure;140
6.1.3.3;Geodesic $\Sigma$-Filter Depending on Image Gradient;140
6.1.3.3.1;Estimation of the Similarity Measure;140
6.1.3.3.2;Design of the Geodesic Filter;141
6.1.3.3.3;Dealing with Multispectral Images;142
6.1.3.4;Geodesic $\Delta$-Filter Accounting for Image Variations;143
6.1.3.5;Experiments;144
6.1.3.5.1;Implementation;144
6.1.3.5.2;Results, Evaluation and Comparison with Other Methods;144
6.1.3.5.3;Limitations and Improvements;145
6.1.3.6;Conclusions;146
6.1.3.7;References;147
6.2;Part II: Image Analysis;13
6.2.1;Computation of Left Ventricular Motion Patterns Using a Normalized Parametric Domain;149
6.2.1.1;Introduction;149
6.2.1.2;Left Ventricular Function Estimation;151
6.2.1.3;Normalized Parametric Domain;154
6.2.1.3.1;Initial Surface Fitting;154
6.2.1.3.2;General Surface Fitting;155
6.2.1.4;Regional Analysis of the LV;156
6.2.1.5;Results;157
6.2.1.5.1;Mean Motion Patterns;159
6.2.1.5.2;Correlation between Motion and HVMB;159
6.2.1.6;Conclusions and Future Work;160
6.2.1.7;References;160
6.2.2;Improving Geodesic Invariant Descriptors through Color Information;162
6.2.2.1;Introduction;162
6.2.2.2;Related Works;163
6.2.2.3;Coloring Geodesic Invariant Features;164
6.2.2.3.1;Fast Marching Algorithm in RGB Space;165
6.2.2.3.2;Building the Geodesic Color Descriptor;166
6.2.2.3.3;Color Invariants Selection;167
6.2.2.4;Experimental Results;170
6.2.2.5;Discussion and Future Works;174
6.2.2.6;References;174
6.2.3;On Head Pose Estimation in Face Recognition;176
6.2.3.1;Introduction;176
6.2.3.2;Feature Extraction;177
6.2.3.2.1;Local Energy Model;177
6.2.3.2.2;Proposed Feature Description;178
6.2.3.3;Pose Estimation;180
6.2.3.3.1;PIE Database;180
6.2.3.3.2;Proposed Approach;181
6.2.3.4;Experimental Setup and Results;185
6.2.3.4.1;Test Results for Seen Imaging Conditions;185
6.2.3.4.2;Test Results for Previously Unseen Illumination Conditions;186
6.2.3.4.3;Test Results for Unseen Poses;187
6.2.3.5;Conclusions and Discussion;187
6.2.3.6;References;188
6.3;Part III: Image Understanding;13
6.3.1;Edge-Based Template Matching with a Harmonic Deformation Model;190
6.3.1.1;Introduction;190
6.3.1.1.1;Related Work;191
6.3.1.1.2;Main Contributions;192
6.3.1.2;Deformable Shape-Based Matching;192
6.3.1.2.1;Shape Model Generation;192
6.3.1.2.2;Deformable Metric Based on Local Edge Patches;193
6.3.1.2.3;Deformable Shape Matching;195
6.3.1.2.4;Harmonic Deformation Model;196
6.3.1.3;Experiments;197
6.3.1.3.1;Comparison with Descriptor-Based Matching;197
6.3.1.3.2;Simulated TPS and Harmonic Deformation;198
6.3.1.3.3;Real World Experiments;199
6.3.1.4;Conclusions;200
6.3.1.5;References;200
6.3.2;Implementation of a Model for Perceptual Completion in $R^2 × S^1$;202
6.3.2.1;Introduction;202
6.3.2.2;Theoretical Background;204
6.3.2.2.1;Lifting of the Image Level Lines in a 3D Space;204
6.3.2.2.2;The Tangent Bundle and the Integral Curves;204
6.3.2.2.3;Curve Length's and Metric of the Space;205
6.3.2.2.4;The Lifted Surface as an Implicit Function;206
6.3.2.2.5;Sub-riemannian Differential Operators;207
6.3.2.2.6;Differential Geometry of the Surface;208
6.3.2.3;The Completion Model;208
6.3.2.3.1;Basic Model;208
6.3.2.3.2;Algorithmic Implementation;209
6.3.2.3.3;Multiple Concentration;210
6.3.2.4;Numerical Scheme;211
6.3.2.5;Experiments and Results;212
6.3.2.5.1;Macula Cieca Example;212
6.3.2.5.2;Occlusion Example;213
6.3.2.6;Conclusions;214
6.3.2.7;References;214
6.3.3;Data Compression - A Generic Principle of Pattern Recognition?;216
6.3.3.1;Introduction;216
6.3.3.2;Compression for Recognition;218
6.3.3.2.1;Method;218
6.3.3.2.2;Theoretical Background;218
6.3.3.2.3;Compression Algorithms;219
6.3.3.2.4;Discussion of the Similarity Measure;219
6.3.3.3;Experiments;219
6.3.3.3.1;Object Recognition;219
6.3.3.3.2;Texture Classification;224
6.3.3.3.3;Image Retrieval;224
6.3.3.4;Conclusions;224
6.3.3.5;References;225
6.3.4;Hierarchical Evaluation Model: Extended Analysis for 3D Face Recognition;227
6.3.4.1;Introduction;227
6.3.4.2;Related Works;228
6.3.4.3;3D Face Matching;229
6.3.4.3.1;The Surface Interpenetration Measure;229
6.3.4.3.2;SA-Based Approach for Range Image Registration;230
6.3.4.3.3;Modified SA-Based Approach for Range Image Registration;231
6.3.4.4;3D Face Authentication;232
6.3.4.4.1;Hierarchical Evaluation Model;233
6.3.4.5;Experimental Results;234
6.3.4.5.1;Alignment Results;234
6.3.4.5.2;Analysis of the Hierarchical Evaluation Model;234
6.3.4.6;Final Remarks;236
6.3.4.7;References;237
6.3.5;Estimation of 3D Instantaneous Motion of a Ball from a Single Motion-Blurred Image;239
6.3.5.1;Introduction;239
6.3.5.1.1;Related Works;240
6.3.5.2;Problem Formulation;241
6.3.5.2.1;Blurred Image Formation;241
6.3.5.2.2;Blur on the Ball Surface;241
6.3.5.3;Image Analysis;242
6.3.5.3.1;Alpha Matting;242
6.3.5.3.2;Blur Analysis;243
6.3.5.4;Reconstruction Technique;243
6.3.5.4.1;Null Translation;244
6.3.5.4.2;Recovering the Ball 3D Position and Velocity;245
6.3.5.4.3;Recovering Spin in the General Case;246
6.3.5.5;Experiments;247
6.3.5.6;Discussion and Conclusions;250
6.3.5.7;References;250
6.4;Part IV: Motion, Tracking and Stereo Vision;13
6.4.1;Integrating Current Weather Effectsin to Urban Visualization;252
6.4.1.1;Introduction;252
6.4.1.2;Related Work;253
6.4.1.3;Data Retrieval and Analysis;254
6.4.1.3.1;Digital 3D City Model;255
6.4.1.3.2;Steerable Weather-Camera;255
6.4.1.3.3;Weather Radar and Web-Based Weather Services;257
6.4.1.4;Rendering Techniques for Certain Weather Effects;259
6.4.1.4.1;Atmospheric Rendering;259
6.4.1.4.2;Rendering of Clouds;259
6.4.1.4.3;Rendering of Rain and Snow;261
6.4.1.4.4;Rendering of Fog;261
6.4.1.5;Subjective Evaluation;261
6.4.1.5.1;Tasks;261
6.4.1.5.2;Results;262
6.4.1.6;Conclusions;263
6.4.1.7;References;264
6.4.2;Guided KLT Tracking Using Camera Parameters in Consideration of Uncertainty;266
6.4.2.1;Introduction;266
6.4.2.1.1;Problem Statement and Motivation;266
6.4.2.1.2;Literature Review;267
6.4.2.2;KLT Tracking;267
6.4.2.3;Using Intrinsic and Extrinsic Camera Parameters;268
6.4.2.4;In Consideration of Uncertainty;269
6.4.2.5;Experimental Results;270
6.4.2.5.1;Trail Length Evaluation;270
6.4.2.5.2;Accuracy Evaluation;272
6.4.2.6;Conclusions and Outlook;274
6.4.2.7;References;275
6.4.3;Automated Object Identification and Position Estimation for Airport Lighting Quality Assessment;276
6.4.3.1;Introduction;276
6.4.3.2;Model-Based (MB) Tracking;278
6.4.3.2.1;Camera Positioning;280
6.4.3.2.2;Distortion Correction;281
6.4.3.2.3;Multi Frame-Based Estimation;282
6.4.3.2.4;Constraints;283
6.4.3.3;Position and Orientation Results;283
6.4.3.4;Luminous Intensity Estimation for Quality Assessment;286
6.4.3.4.1;Luminous Intensity Estimation Results;286
6.4.3.5;Concluding Remarks;288
6.4.3.6;References;289
7;Author Index;290