by Roland Siu-kwong Ip. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 101-[105]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Image Compression --- p.2 / Chapter 1.2.1 --- Classification of Image Compression --- p.2 / Chapter 1.2.2 --- Lossy Compression Approaches --- p.3 / Chapter 1.3 --- Video Compression --- p.4 / Chapter 1.3.1 --- Video Compression System --- p.5 / Chapter 1.4 --- Stereoscopic Video Compression --- p.6 / Chapter 1.5 --- Organization of the thesis --- p.6 / Chapter 2 --- Motion Video Coding Theory --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- Representations --- p.8 / Chapter 2.2.1 --- Temporal Processing --- p.13 / Chapter 2.2.2 --- Spatial Processing --- p.19 / Chapter 2.3 --- Quantization --- p.25 / Chapter 2.3.1 --- Scalar Quantization --- p.25 / Chapter 2.3.2 --- Vector Quantization --- p.27 / Chapter 2.4 --- Code Word Assignment --- p.29 / Chapter 2.5 --- Selection of Video Coding Standard --- p.31 / Chapter 3 --- MPEG Compatible Stereoscopic Coding --- p.34 / Chapter 3.1 --- Introduction --- p.34 / Chapter 3.2 --- MPEG Compatibility --- p.36 / Chapter 3.3 --- Stereoscopic Video Coding --- p.37 / Chapter 3.3.1 --- Coding by Stereoscopic Differences --- p.37 / Chapter 3.3.2 --- I-pictures only Disparity Coding --- p.40 / Chapter 3.4 --- Stereoscopic MPEG Encoder --- p.44 / Chapter 3.4.1 --- Stereo Disparity Estimator --- p.45 / Chapter 3.4.2 --- Improved Disparity Estimation --- p.47 / Chapter 3.4.3 --- Stereo Bitstream Multiplexer --- p.49 / Chapter 3.5 --- Generic Implementation --- p.50 / Chapter 3.5.1 --- Macroblock Converter --- p.54 / Chapter 3.5.2 --- DCT Functional Block --- p.55 / Chapter 3.5.3 --- Rate Control --- p.57 / Chapter 3.6 --- Stereoscopic MPEG Decoder --- p.58 / Chapter 3.6.1 --- Mono Playback --- p.58 / Chapter 3.6.2 --- Stereo Playback --- p.60 / Chapter 4 --- Performance Evaluation --- p.63 / Chapter 4.1 --- Introduction --- p.63 / Chapter 4.2 --- Test Sequences Generation --- p.63 / Chapter 4.3 --- Simulation Environment --- p.64 / Chapter 4.4 --- Simulation Results --- p.65 / Chapter 4.4.1 --- Objective Results --- p.65 / Chapter 4.4.2 --- Subjective Results --- p.72 / Chapter 5 --- Conclusions --- p.80 / Chapter A --- MPEG ´ؤ An International Standard --- p.83 / Chapter A.l --- Introduction --- p.83 / Chapter A.2 --- Preprocessing --- p.84 / Chapter A.3 --- Data Structure of Pictures --- p.85 / Chapter A.4 --- Picture Coding --- p.86 / Chapter A.4.1 --- Coding of Motion Vectors --- p.90 / Chapter A.4.2 --- Coding of Quantized Coefficients --- p.94 / References --- p.101

by Wai-Wa Wilson Chan. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 79-80). / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Problem under study and scope --- p.4 / Chapter 1.2 --- Review of relevant research --- p.6 / Chapter 1.3 --- Objectives --- p.11 / Chapter 2. --- Theory --- p.12 / Chapter 2.1 --- Multi-resolution representation of digital video --- p.13 / Chapter 2.2 --- Performance measure of progressive algorithm --- p.15 / Chapter 2.3 --- Introduction to depth pyramid --- p.35 / Chapter 2.4 --- Introduction to spatial pyramid --- p.37 / Chapter 2.5 --- Introduction to temporal pyramid --- p.42 / Chapter 2.6 --- Proposed algorithm for progressive transmission using depth-spatial-temporal pyramid --- p.46 / Chapter 3. --- Experiment --- p.55 / Chapter 3.1 --- Simulation on depth pyramid --- p.59 / Chapter 3.2 --- Simulation on spatial pyramid --- p.60 / Chapter 3.3 --- Simulation on temporal pyramid --- p.62 / Chapter 3.4 --- Simulation on algorithm for progressive transmission using depth-spatial-temporal pyramid --- p.64 / Chapter 4. --- Conclusions and discussions --- p.74 / Chapter 5. --- Reference and Appendix --- p.79

Wai-kin Vong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 87-[91]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Three Dimensional Image --- p.1 / Chapter 1.2 --- Definition of segmentation --- p.2 / Chapter 1.3 --- 3D Image Segmentation --- p.3 / Chapter 1.4 --- Image Splitting Operation --- p.4 / Chapter 1.5 --- Region Merging Operation --- p.4 / Chapter 1.6 --- Split-and-merge Segmentation --- p.4 / Chapter 1.6.1 --- Selection of particular operators --- p.5 / Chapter 2 --- Overview of Image Segmentation Techniques --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- Edge Based Method --- p.6 / Chapter 2.2.1 --- 3D Laplacian of Gaussian Filtering --- p.7 / Chapter 2.2.2 --- 3D Deformable Surfaces [8] --- p.11 / Chapter 2.3 --- Region Based Method --- p.14 / Chapter 2.3.1 --- 3D oct-tree split-and-merge --- p.15 / Chapter 2.3.2 --- 3D pyramid segmentation --- p.17 / Chapter 2.4 --- 2D segmentation Approaches --- p.20 / Chapter 2.4.1 --- 2D Image segmentation by shape description --- p.20 / Chapter 2.4.2 --- Morphological Watershed Transform (WT) --- p.23 / Chapter 2.5 --- Discussion --- p.34 / Chapter 3 --- Modification Of Digital Watershed Transform (DWT) --- p.36 / Chapter 3.1 --- Introduction --- p.36 / Chapter 3.2 --- Edge Detection --- p.37 / Chapter 3.2.1 --- Discrete Non-linear Edge Detectors --- p.37 / Chapter 3.2.2 --- Canny's Edge Detector --- p.40 / Chapter 3.2.3 --- Gradient of Gaussian Filter --- p.42 / Chapter 3.3 --- Digital Watershed Transform --- p.46 / Chapter 3.3.1 --- Introduction --- p.46 / Chapter 3.3.2 --- Modification of SKIZ --- p.46 / Chapter 3.3.3 --- Implementation --- p.51 / Chapter 4 --- Region Modeling --- p.55 / Chapter 4.1 --- Introduction --- p.55 / Chapter 4.2 --- Texture Definition --- p.57 / Chapter 4.3 --- Texture Modeling --- p.58 / Chapter 4.3.1 --- Markov Random Field (MRF) --- p.58 / Chapter 4.3.2 --- Simultaneous Autoregressive (SAR) Model --- p.59 / Chapter 4.3.3 --- Parameter Estimation --- p.61 / Chapter 4.3.4 --- A Simple model --- p.63 / Chapter 4.3.5 --- Combination of MRF parameters --- p.63 / Chapter 4.3.6 --- Similarity Measure --- p.66 / Chapter 4.4 --- Model Evaluation --- p.68 / Chapter 4.4.1 --- Classification of Different Materials --- p.68 / Chapter 4.4.2 --- Rotational Invariance --- p.69 / Chapter 4.5 --- Results and Observations --- p.72 / Chapter 5 --- Three-Dimensional Segmentation with Interactive Labeling --- p.73 / Chapter 5.1 --- Introduction --- p.73 / Chapter 5.2 --- Region Merging Scheme --- p.75 / Chapter 5.3 --- Interactive Labeling --- p.76 / Chapter 5.4 --- Experiment of 3D Guided Segmentation --- p.77 / Chapter 6 --- Conclusion --- p.81 / Chapter 6.1 --- Image Partitioning by Watershed Transform --- p.81 / Chapter 6.2 --- Image modeling by Markov Random Field --- p.82 / Chapter 6.3 --- 3D image segmentation --- p.82 / A --- p.84 / B --- p.86 / Bibliography --- p.87

Fung Yiu-fai Phineas. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 142-148). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Model-based Pose Estimation --- p.3 / Chapter 1.1.1 --- Application - 3D Motion Tracking --- p.4 / Chapter 1.2 --- Image-based View Synthesis --- p.4 / Chapter 1.3 --- Thesis Contribution --- p.7 / Chapter 1.4 --- Thesis Outline --- p.8 / Chapter 2 --- General Background --- p.9 / Chapter 2.1 --- Notations --- p.9 / Chapter 2.2 --- Camera Models --- p.10 / Chapter 2.2.1 --- Generic Camera Model --- p.10 / Chapter 2.2.2 --- Full-perspective Camera Model --- p.11 / Chapter 2.2.3 --- Affine Camera Model --- p.12 / Chapter 2.2.4 --- Weak-perspective Camera Model --- p.13 / Chapter 2.2.5 --- Paraperspective Camera Model --- p.14 / Chapter 2.3 --- Model-based Motion Analysis --- p.15 / Chapter 2.3.1 --- Point Correspondences --- p.16 / Chapter 2.3.2 --- Line Correspondences --- p.18 / Chapter 2.3.3 --- Angle Correspondences --- p.19 / Chapter 2.4 --- Panoramic Representation --- p.20 / Chapter 2.4.1 --- Static Mosaic --- p.21 / Chapter 2.4.2 --- Dynamic Mosaic --- p.22 / Chapter 2.4.3 --- Temporal Pyramid --- p.23 / Chapter 2.4.4 --- Spatial Pyramid --- p.23 / Chapter 2.5 --- Image Pre-processing --- p.24 / Chapter 2.5.1 --- Feature Extraction --- p.24 / Chapter 2.5.2 --- Spatial Filtering --- p.27 / Chapter 2.5.3 --- Local Enhancement --- p.31 / Chapter 2.5.4 --- Dynamic Range Stretching or Compression --- p.32 / Chapter 2.5.5 --- YIQ Color Model --- p.33 / Chapter 3 --- Model-based Pose Estimation --- p.35 / Chapter 3.1 --- Previous Work --- p.35 / Chapter 3.1.1 --- Estimation from Established Correspondences --- p.36 / Chapter 3.1.2 --- Direct Estimation from Image Intensities --- p.49 / Chapter 3.1.3 --- Perspective-3-Point Problem --- p.51 / Chapter 3.2 --- Our Iterative P3P Algorithm --- p.58 / Chapter 3.2.1 --- Gauss-Newton Method --- p.60 / Chapter 3.2.2 --- Dealing with Ambiguity --- p.61 / Chapter 3.2.3 --- 3D-to-3D Motion Estimation --- p.66 / Chapter 3.3 --- Experimental Results --- p.68 / Chapter 3.3.1 --- Synthetic Data --- p.68 / Chapter 3.3.2 --- Real Images --- p.72 / Chapter 3.4 --- Discussions --- p.73 / Chapter 4 --- Panoramic View Analysis --- p.76 / Chapter 4.1 --- Advanced Mosaic Representation --- p.76 / Chapter 4.1.1 --- Frame Alignment Policy --- p.77 / Chapter 4.1.2 --- Multi-resolution Representation --- p.77 / Chapter 4.1.3 --- Parallax-based Representation --- p.78 / Chapter 4.1.4 --- Multiple Moving Objects --- p.79 / Chapter 4.1.5 --- Layers and Tiles --- p.79 / Chapter 4.2 --- Panorama Construction --- p.79 / Chapter 4.2.1 --- Image Acquisition --- p.80 / Chapter 4.2.2 --- Image Alignment --- p.82 / Chapter 4.2.3 --- Image Integration --- p.88 / Chapter 4.2.4 --- Significant Residual Estimation --- p.89 / Chapter 4.3 --- Advanced Alignment Algorithms --- p.90 / Chapter 4.3.1 --- Patch-based Alignment --- p.91 / Chapter 4.3.2 --- Global Alignment (Block Adjustment) --- p.92 / Chapter 4.3.3 --- Local Alignment (Deghosting) --- p.93 / Chapter 4.4 --- Mosaic Application --- p.94 / Chapter 4.4.1 --- Visualization Tool --- p.94 / Chapter 4.4.2 --- Video Manipulation --- p.95 / Chapter 4.5 --- Experimental Results --- p.96 / Chapter 5 --- Panoramic Walkthrough --- p.99 / Chapter 5.1 --- Problem Statement and Notations --- p.100 / Chapter 5.2 --- Previous Work --- p.101 / Chapter 5.2.1 --- 3D Modeling and Rendering --- p.102 / Chapter 5.2.2 --- Branching Movies --- p.103 / Chapter 5.2.3 --- Texture Window Scaling --- p.104 / Chapter 5.2.4 --- Problems with Simple Texture Window Scaling --- p.105 / Chapter 5.3 --- Our Walkthrough Approach --- p.106 / Chapter 5.3.1 --- Cylindrical Projection onto Image Plane --- p.106 / Chapter 5.3.2 --- Generating Intermediate Frames --- p.108 / Chapter 5.3.3 --- Occlusion Handling --- p.114 / Chapter 5.4 --- Experimental Results --- p.116 / Chapter 5.5 --- Discussions --- p.116 / Chapter 6 --- Conclusion --- p.121 / Chapter A --- Formulation of Fischler and Bolles' Method for P3P Problems --- p.123 / Chapter B --- Derivation of z1 and z3 in terms of z2 --- p.127 / Chapter C --- Derivation of e1 and e2 --- p.129 / Chapter D --- Derivation of the Update Rule for Gauss-Newton Method --- p.130 / Chapter E --- Proof of (λ1λ2-λ 4）>〉0 --- p.132 / Chapter F --- Derivation of φ and hi --- p.133 / Chapter G --- Derivation of w1j to w4j --- p.134 / Chapter H --- More Experimental Results on Panoramic Stitching Algorithms --- p.138 / Bibliography --- p.148

Ma, Ka Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 82-87). / Abstract and appendix in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objective of This Research --- p.4 / Chapter 1.2 --- Organization of This Thesis --- p.5 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Textual - Image Query Approach --- p.7 / Chapter 2.1.1 --- Yahoo! Image Surfer --- p.7 / Chapter 2.1.2 --- QBIC (Query By Image Content) --- p.8 / Chapter 2.2 --- Feature-based Approach --- p.9 / Chapter 2.2.1 --- Texture Thesaurus for Aerial Photos --- p.9 / Chapter 2.3 --- Caption-aided Approach --- p.10 / Chapter 2.3.1 --- PICTION (Picture and capTION) --- p.10 / Chapter 2.3.2 --- MARIE --- p.11 / Chapter 2.4 --- Summary --- p.11 / Chapter 3 --- Caption Generation --- p.13 / Chapter 3.1 --- System Architecture --- p.13 / Chapter 3.2 --- Domain Pool --- p.15 / Chapter 3.3 --- Image Feature Extraction --- p.16 / Chapter 3.3.1 --- Preprocessing --- p.16 / Chapter 3.3.2 --- Image Segmentation --- p.17 / Chapter 3.4 --- Classification --- p.24 / Chapter 3.4.1 --- Self-Organizing Map (SOM) --- p.26 / Chapter 3.4.2 --- Learning Vector Quantization (LVQ) --- p.28 / Chapter 3.4.3 --- Output of the Classification --- p.30 / Chapter 3.5 --- Caption Generation --- p.30 / Chapter 3.5.1 --- Phase One: Logical Form Generation --- p.31 / Chapter 3.5.2 --- Phase Two: Simplification --- p.32 / Chapter 3.5.3 --- Phase Three: Captioning --- p.33 / Chapter 3.6 --- Summary --- p.35 / Chapter 4 --- Query Examples --- p.37 / Chapter 4.1 --- Query Types --- p.37 / Chapter 4.1.1 --- Non-content-based Retrieval --- p.38 / Chapter 4.1.2 --- Content-based Retrieval --- p.38 / Chapter 4.2 --- Hierarchy Graph --- p.41 / Chapter 4.3 --- Matching --- p.42 / Chapter 4.4 --- Summary --- p.48 / Chapter 5 --- Evaluation --- p.49 / Chapter 5.1 --- Experimental Set-up --- p.50 / Chapter 5.2 --- Experimental Results --- p.51 / Chapter 5.2.1 --- Segmentation --- p.51 / Chapter 5.2.2 --- Classification --- p.53 / Chapter 5.2.3 --- Captioning --- p.55 / Chapter 5.2.4 --- Overall Performance --- p.56 / Chapter 5.3 --- Observations --- p.57 / Chapter 5.4 --- Summary --- p.58 / Chapter 6 --- Another Application --- p.59 / Chapter 6.1 --- Police Force Crimes Investigation --- p.59 / Chapter 6.1.1 --- Image Feature Extraction --- p.61 / Chapter 6.1.2 --- Caption Generation --- p.64 / Chapter 6.1.3 --- Query --- p.66 / Chapter 6.2 --- An Illustrative Example --- p.68 / Chapter 6.3 --- Summary --- p.72 / Chapter 7 --- Conclusions --- p.74 / Chapter 7.1 --- Contribution --- p.77 / Chapter 7.2 --- Future Work --- p.78 / Bibliography --- p.81 / Appendices --- p.88 / Chapter A --- Segmentation Result Under Different Parametes --- p.89 / Chapter B --- Segmentation Time of 10 Randomly Selected Images --- p.90 / Chapter C --- Sample Captions --- p.93

Chow Chi Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 136-139). / Text in English; abstracts in English and Chinese. / Chow Chi Kin. / Abstract --- p.i / 摘要 --- p.ii / Acknowledgement --- p.iii / Table of content --- p.iv / List of figures --- p.viii / Chapter Chapter 1: --- Introduction --- p.1 / Chapter Chapter 2: --- What is Surface Registration? --- p.7 / Chapter 2.1 --- What is free-form surface --- p.7 / Chapter 2.2 --- Methodologies of surface construction --- p.8 / Chapter 2.2.1 --- CAD Model --- p.9 / Chapter 2.2.2 --- 3D Reconstruction algorithm --- p.11 / Chapter 2.2.3 --- Geometric/Range sensors --- p.12 / Chapter 2.3 --- What is Surface Registration --- p.14 / Chapter 2.4 --- Purpose of Surface Registration --- p.19 / Chapter 2.5 --- Review of exist registration algorithms --- p.20 / Chapter 2.5.1 --- ICP (Iterative Closest Point) algorithm --- p.20 / Chapter A. --- Overview of ICP --- p.21 / Chapter B. --- ICP Kernel --- p.22 / Chapter C. --- Possible pre-processings of ICP --- p.23 / Chapter D. --- Multiple features in the closest point search --- p.24 / Chapter E. --- Advantages of ICP --- p.25 / Chapter F --- . Disadvantages of ICP --- p.26 / Chapter 2.5.2 --- "Feature-based, registration" --- p.27 / Chapter 2.5.3 --- Genetic algorithm approach --- p.28 / Chapter Chapter 3: --- What is Genetic Algorithm? --- p.32 / Chapter 3.1 --- What is Genetic Algorithm (GA) --- p.32 / Chapter 3.2 --- Exists Search Methods --- p.33 / Chapter 3.3 --- Mechanism of Genetic Algorithms --- p.39 / Chapter 3.3.1 --- Initialization --- p.41 / Chapter 3.3.2 --- Reproduction --- p.41 / Chapter A. --- Cross-Over --- p.42 / Chapter B. --- Mutation --- p.44 / Chapter 3.3.3 --- Selection --- p.47 / Chapter A. --- Best Fitness Selection --- p.47 / Chapter B. --- Probabilistic Selection --- p.49 / Chapter 3.3.4 --- Termination checking --- p.52 / Chapter A. --- Fixed number of generation --- p.53 / Chapter B. --- Adaptive number of generation --- p.53 / Chapter 3.3.5 --- Solution Space --- p.54 / Chapter 3.3.6 --- Formation of chromosome --- p.54 / Chapter 3.3.7 --- Fitness function --- p.55 / Chapter 3.4 --- Examples of function optimization using Genetic Algorithm --- p.55 / Experiment 1: 2D Sphere function --- p.56 / Experiment 2: 2D Sinc function --- p.58 / Experiment 3: Foxholes function --- p.61 / Experiment 4: Steps Exponential Decay function --- p.63 / Chapter 3.5 --- Convergence Theorem of Genetic Algorithm --- p.66 / Chapter Chapter 4: --- Surface Registration as an Optimization --- p.71 / Chapter 4.1 --- Solution space --- p.75 / Chapter 4.2 --- Formation of Gene and Chromosome --- p.79 / Chapter 4.3 --- Fitness Function --- p.83 / Chapter 4.4 --- Genetic Algorithm VS. Steepest Gradient Descent --- p.88 / Chapter 4.5 --- Reproduction --- p.94 / Chapter 4.5.1 --- Cross-Over --- p.94 / Chapter 4.5.2 --- Mutation --- p.95 / Chapter 4.5.3 --- Variant --- p.95 / Chapter 4.6 --- Selection --- p.101 / Chapter 4.7 --- Dynamic Boundary --- p.103 / Chapter 4.8 --- Nearest Neighbor Search --- p.108 / Chapter Chapter 5: --- Experimental Results --- p.112 / Chapter 5.1 --- Surface Registration --- p.113 / Chapter 5.2 --- Model Integration --- p.114 / Chapter A. --- Human skull --- p.115 / Chapter B. --- Easter Island stone --- p.115 / Chapter C. --- Child --- p.116 / Chapter D. --- Dinosaur head --- p.116 / Chapter 5.3 --- Noise Sensitivity --- p.117 / Chapter 5.3.1 --- Addition of Gaussian noise --- p.117 / Chapter A. --- Human Skull --- p.117 / Chapter B. --- Human foot bone --- p.119 / Chapter C. --- Human heart --- p.120 / Chapter D. --- Human vertebrae --- p.122 / Chapter E. --- Human fetus --- p.125 / Chapter 5.3.2 --- Smoothing of pre-added Gaussian noise --- p.127 / Chapter A. --- Human skull --- p.128 / Chapter B. --- Human foot bone --- p.129 / Chapter 5.4 --- Model Integration of Real Images --- p.130 / Chapter Chapter 6: --- Conclusion --- p.133 / References --- p.136

by Andrew L. Arengo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 65-66). / Abstract also in Chinese. / Acknowledgments --- p.ii / List Of Figures --- p.v / Abstract --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.2 / Chapter 1.2 --- Approach of This Thesis and Contributions --- p.3 / Chapter 1.3 --- Organization of This Thesis --- p.4 / Chapter 2 --- Previous Work --- p.6 / Chapter 2.1 --- Using Grouped Features --- p.6 / Chapter 2.2 --- Applying Additional Heuristics --- p.7 / Chapter 2.3 --- Homography and Related Works --- p.9 / Chapter 3 --- Theory and Problem Formulation --- p.10 / Chapter 3.1 --- Overview of the Problems --- p.10 / Chapter 3.1.1 --- Preprocessing --- p.10 / Chapter 3.1.2 --- Establishing Correspondences --- p.11 / Chapter 3.1.3 --- Recovering 3D Depth --- p.14 / Chapter 3.2 --- Solving the Correspondence Problem --- p.15 / Chapter 3.2.1 --- Epipolar Constraint --- p.15 / Chapter 3.2.2 --- Surface-Continuity and Feature-Ordering Heuristics --- p.16 / Chapter 3.2.3 --- Using the Concept of Homography --- p.18 / Chapter 3.3 --- Concept of Homography --- p.20 / Chapter 3.3.1 --- Barycentric Coordinate System --- p.20 / Chapter 3.3.2 --- Image to Image Mapping of the Same Plane --- p.22 / Chapter 3.4 --- Problem Formulation --- p.23 / Chapter 3.4.1 --- Preliminaries --- p.23 / Chapter 3.4.2 --- Case of Single Planar Surface --- p.24 / Chapter 3.4.3 --- Case of Multiple Planar Surfaces --- p.28 / Chapter 3.5 --- Subspace Clustering --- p.28 / Chapter 3.6 --- Overview of the Approach --- p.30 / Chapter 4 --- Experimental Results --- p.33 / Chapter 4.1 --- Synthetic Images --- p.33 / Chapter 4.2 --- Aerial Images --- p.36 / Chapter 4.2.1 --- T-shape building --- p.38 / Chapter 4.2.2 --- Rectangular Building --- p.39 / Chapter 4.2.3 --- 3-layers Building --- p.40 / Chapter 4.2.4 --- Pentagon --- p.44 / Chapter 4.3 --- Indoor Scenes --- p.52 / Chapter 4.3.1 --- Stereo Motion Pair --- p.53 / Chapter 4.3.2 --- Hallway Scene --- p.56 / Chapter 5 --- Summary and Conclusions --- p.63

by Ho Pui-Kuen, Patrick. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 57-59). / Abstract also in Chinese. / Acknowledgments --- p.ii / List Of Figures --- p.v / List Of Tables --- p.vi / Abstract --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Moviation of Problem --- p.1 / Chapter 1.2 --- Our Approach and Summary of Contributions --- p.3 / Chapter 1.3 --- Organization of this Thesis --- p.4 / Chapter 2 --- Previous Work --- p.5 / Chapter 3 --- Structure Recovery from Stereo-Motion Images --- p.7 / Chapter 3.1 --- Motion Model --- p.8 / Chapter 3.2 --- Stereo-Motion Model --- p.10 / Chapter 3.3 --- Inferring Stereo Correspondences --- p.13 / Chapter 3.4 --- Determining 3D Structure from One Stereo Pair --- p.17 / Chapter 3.5 --- Computational Complexity of Inference Process --- p.18 / Chapter 4 --- Experimental Results --- p.19 / Chapter 4.1 --- Synthetic Images and Statistical Results --- p.19 / Chapter 4.2 --- Real Image Sequences --- p.21 / Chapter 4.2.1 --- House Model' Image Sequences --- p.22 / Chapter 4.2.2 --- Oscilloscope and Soda Can' Image Sequences --- p.23 / Chapter 4.2.3 --- Bowl' Image Sequences --- p.24 / Chapter 4.2.4 --- Building' Image Sequences --- p.27 / Chapter 4.3 --- Computational Time of Experiments --- p.28 / Chapter 5 --- Determining Motion and Structure from All Stereo Pairs --- p.30 / Chapter 5.1 --- Determining Motion and Structure --- p.31 / Chapter 5.2 --- Identifying Incorrect Motion Correspondences --- p.33 / Chapter 6 --- More Experiments --- p.34 / Chapter 6.1 --- Synthetic Cube' Images --- p.34 / Chapter 6.2 --- Snack Bag´ة Image Sequences --- p.35 / Chapter 6.3 --- Comparison with Structure Recovered from One Stereo Pair --- p.37 / Chapter 7 --- Conclusion --- p.41 / Chapter A --- Basic Concepts in Computer Vision --- p.43 / Chapter A.1 --- Camera Projection Model --- p.43 / Chapter A.2 --- Epipolar Constraint in Stereo Vision --- p.47 / Chapter B --- Inferring Stereo Correspondences with Matrices of Rank < 4 --- p.49 / Chapter C --- Generating Image Reprojection --- p.51 / Chapter D --- Singular Value Decomposition --- p.53 / Chapter E --- Quaternion --- p.55

by Kevin Chun-Ho Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 74-79). / Abstract also in Chinese. / Chinese Abstract --- p.v / Abstract --- p.vi / Acknowledgements --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Prior Work : Image Segmentation Techniques --- p.3 / Chapter 2.1 --- Introduction to Image Segmentation --- p.4 / Chapter 2.2 --- Region Based Segmentation --- p.5 / Chapter 2.2.1 --- Boundary Based vs Region Based --- p.5 / Chapter 2.2.2 --- Region growing --- p.5 / Chapter 2.2.3 --- Integrating Region Based and Edge Detection --- p.6 / Chapter 2.2.4 --- Watershed Based Methods --- p.8 / Chapter 2.3 --- Fuzzy Set Theory in Segmentation --- p.8 / Chapter 2.3.1 --- Fuzzy Geometry Concept --- p.8 / Chapter 2.3.2 --- Fuzzy C-Means (FCM) Clustering --- p.9 / Chapter 2.4 --- Canny edge filter with contour following --- p.11 / Chapter 2.5 --- Pyramid based Fast Curve Extraction --- p.12 / Chapter 2.6 --- Curve Extraction with Multi-Resolution Fourier transformation --- p.13 / Chapter 2.7 --- User interfaces for Image Segmentation --- p.13 / Chapter 2.7.1 --- Intelligent Scissors --- p.14 / Chapter 2.7.2 --- Magic Wands --- p.16 / Chapter 3 --- Prior Work : Active Contours Model (Snakes) --- p.17 / Chapter 3.1 --- Introduction to Active Contour Model --- p.18 / Chapter 3.2 --- Variants and Extensions of Snakes --- p.19 / Chapter 3.2.1 --- Balloons --- p.20 / Chapter 3.2.2 --- Robust Dual Active Contour --- p.21 / Chapter 3.2.3 --- Gradient Vector Flow Snakes --- p.22 / Chapter 3.2.4 --- Energy Minimization using Dynamic Programming with pres- ence of hard constraints --- p.23 / Chapter 3.3 --- Conclusions --- p.25 / Chapter 4 --- Slimmed Graph --- p.26 / Chapter 4.1 --- BSP-based image analysis --- p.27 / Chapter 4.2 --- Split Line Selection --- p.29 / Chapter 4.3 --- Split Line Selection with Summed Area Table --- p.29 / Chapter 4.4 --- Neighbor blocks --- p.31 / Chapter 4.5 --- Slimmed Graph Generation --- p.32 / Chapter 4.6 --- Time Complexity --- p.35 / Chapter 4.7 --- Results and Conclusions --- p.36 / Chapter 5 --- Fast Intelligent Scissor --- p.38 / Chapter 5.1 --- Background --- p.39 / Chapter 5.2 --- Motivation of Fast Intelligent Scissors --- p.39 / Chapter 5.3 --- Main idea of Fast Intelligent Scissors --- p.40 / Chapter 5.3.1 --- Node position and Cost function --- p.41 / Chapter 5.4 --- Implementation and Results --- p.42 / Chapter 5.5 --- Conclusions --- p.43 / Chapter 6 --- 3D Contour Detection: Volume Cutting --- p.50 / Chapter 6.1 --- Interactive Volume Cutting with the intelligent scissors --- p.51 / Chapter 6.2 --- Contour Selection --- p.52 / Chapter 6.2.1 --- 3D Intelligent Scissors --- p.53 / Chapter 6.2.2 --- Dijkstra's algorithm --- p.54 / Chapter 6.3 --- 3D Volume Cutting --- p.54 / Chapter 6.3.1 --- Cost function for the cutting surface --- p.55 / Chapter 6.3.2 --- "Continuity function (x,y, z) " --- p.59 / Chapter 6.3.3 --- Finding the cutting surface --- p.61 / Chapter 6.3.4 --- Topological problems for the volume cutting --- p.61 / Chapter 6.3.5 --- Assumptions for the well-conditional contour used in our algo- rithm --- p.62 / Chapter 6.4 --- Implementation and Results --- p.64 / Chapter 6.5 --- Conclusions --- p.64 / Chapter 7 --- Conclusions --- p.71 / Chapter 7.1 --- Contributions --- p.71 / Chapter 7.2 --- Future Work --- p.72 / Chapter 7.2.1 --- Real-time interactive tools with Slimmed Graph --- p.72 / Chapter 7.2.2 --- 3D slimmed graph --- p.72 / Chapter 7.2.3 --- Cartoon Film Generation System --- p.72

Wei Dan. / Thesis submitted in: October 2005. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 93-101). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Our applications --- p.1 / Chapter 1.3 --- Structure of the thesis --- p.2 / Chapter 2 --- The Programmable Graphics Hardware --- p.4 / Chapter 2.1 --- Introduction --- p.4 / Chapter 2.2 --- The evolution of programmable graphics hardware --- p.4 / Chapter 2.3 --- Benefit of GPU --- p.6 / Chapter 2.4 --- Architecture of programmable graphics hardware --- p.9 / Chapter 2.4.1 --- The graphics hardware pipeline --- p.9 / Chapter 2.4.2 --- Programmable graphics hardware --- p.10 / Chapter 2.5 --- Data Mapping in GPU --- p.12 / Chapter 2.6 --- Some limitations of current GPU --- p.13 / Chapter 2.7 --- Application and Related Work --- p.16 / Chapter 3 --- Image-based Relighting on GPU --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- Image based relighting --- p.20 / Chapter 3.2.1 --- The plenoptic illumination function --- p.20 / Chapter 3.2.2 --- Sampling and Relighting --- p.21 / Chapter 3.3 --- Linear Approximation Function --- p.22 / Chapter 3.3.1 --- Spherical harmonics basis function --- p.22 / Chapter 3.3.2 --- Radial basis function --- p.23 / Chapter 3.4 --- Data Representation --- p.23 / Chapter 3.5 --- Relighting on GPU --- p.24 / Chapter 3.5.1 --- Directional light source relighting --- p.27 / Chapter 3.5.2 --- Point light source relighting --- p.28 / Chapter 3.6 --- Experiment --- p.32 / Chapter 3.6.1 --- Visual Evaluation --- p.32 / Chapter 3.6.2 --- Statistic Evaluation --- p.33 / Chapter 3.7 --- Conclusion --- p.34 / Chapter 4 --- Texture Compression on GPU --- p.40 / Chapter 4.1 --- Introduction --- p.40 / Chapter 4.2 --- The Feature of Texture Compression --- p.41 / Chapter 4.3 --- Implementation --- p.42 / Chapter 4.3.1 --- Encoding --- p.43 / Chapter 4.3.2 --- Decoding --- p.46 / Chapter 4.4 --- The Texture Compression based Relighting on GPU --- p.46 / Chapter 4.5 --- An improvement of the existing compression techniques --- p.48 / Chapter 4.6 --- Experiment Evaluation --- p.50 / Chapter 4.7 --- Conclusion --- p.51 / Chapter 5 --- Environment Relighting on GPU --- p.55 / Chapter 5.1 --- Overview --- p.55 / Chapter 5.2 --- Related Work --- p.56 / Chapter 5.3 --- Linear Approximation Algorithm --- p.58 / Chapter 5.3.1 --- Basic Architecture --- p.58 / Chapter 5.3.2 --- Relighting on SH --- p.60 / Chapter 5.3.3 --- Relighting on RBF --- p.61 / Chapter 5.3.4 --- Sampling the Environment --- p.63 / Chapter 5.4 --- Implementation on GPU --- p.64 / Chapter 5.5 --- Evaluation --- p.66 / Chapter 5.5.1 --- Visual evaluation --- p.66 / Chapter 5.5.2 --- Statistic evaluation --- p.67 / Chapter 5.6 --- Conclusion --- p.69 / Chapter 6 --- Neocognitron on GPU --- p.70 / Chapter 6.1 --- Overview --- p.70 / Chapter 6.2 --- Neocognitron --- p.72 / Chapter 6.3 --- Neocognitron on GPU --- p.75 / Chapter 6.3.1 --- Data Mapping and Connection Texture --- p.76 / Chapter 6.3.2 --- Convolution and Offset Computation --- p.77 / Chapter 6.3.3 --- Recognition Pipeline --- p.80 / Chapter 6.4 --- Experiments and Results --- p.81 / Chapter 6.4.1 --- Performance Evaluation --- p.81 / Chapter 6.4.2 --- Feature Visualization of Intermediate-Layer --- p.84 / Chapter 6.4.3 --- A Real-Time Tracking Test --- p.84 / Chapter 6.5 --- Conclusion --- p.87 / Chapter 7 --- Conclusion --- p.90 / Bibliography --- p.93