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

Cheung Man-kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 116-120). / Abstracts in English and Chinese. / Chapter 1) --- Introduction --- p.1 / Chapter 1.1) --- Building of the 3D Wireframe and Facial Model --- p.2 / Chapter 1.2) --- Description of 3D Model Based Video Conferencing --- p.3 / Chapter 1.3) --- Wireframe Model Fitting or Conformation --- p.6 / Chapter 1.4) --- Pose Estimation --- p.8 / Chapter 1.5) --- Facial Motion Estimation and Synthesis --- p.9 / Chapter 1.6) --- Thesis Outline --- p.10 / Chapter 2) --- Wireframe model Fitting --- p.11 / Chapter 2.1) --- Algorithm of WFM Fitting --- p.12 / Chapter 2.1.1) --- Global Deformation --- p.14 / Chapter a) --- Scaling --- p.14 / Chapter b) --- Shifting --- p.15 / Chapter 2.1.2) --- Local Deformation --- p.15 / Chapter a) --- Shifting --- p.16 / Chapter b) --- Scaling --- p.17 / Chapter 2.1.3) --- Fine Updating --- p.17 / Chapter 2.2) --- Steps of Fitting --- p.18 / Chapter 2.3) --- Functions of Different Deformation --- p.18 / Chapter 2.4) --- Experimental Results --- p.19 / Chapter 2.4.1) --- Output wireframe in each step --- p.19 / Chapter 2.4.2) --- Examples of Mis-fitted wireframe with incoming image --- p.22 / Chapter 2.4.3) --- Fitted 3D facial wireframe --- p.23 / Chapter 2.4.4) --- Effect of mis-fitted wireframe after compensation of motion --- p.24 / Chapter 2.5) --- Summary --- p.26 / Chapter 3) --- Epipolar Geometry --- p.27 / Chapter 3.1) --- Pinhole Camera Model and Perspective Projection --- p.28 / Chapter 3.2) --- Concepts in Epipolar Geometry --- p.31 / Chapter 3.2.1) --- Working with normalized image coordinates --- p.33 / Chapter 3.2.2) --- Working with pixel image coordinates --- p.35 / Chapter 3.2.3) --- Summary --- p.37 / Chapter 3.3) --- 8-point Algorithm (Essential and Fundamental Matrix) --- p.38 / Chapter 3.3.1) --- Outline of the 8-point algorithm --- p.38 / Chapter 3.3.2) --- Modification on obtained Fundamental Matrix --- p.39 / Chapter 3.3.3) --- Transformation of Image Coordinates --- p.40 / Chapter a) --- Translation to mean of points --- p.40 / Chapter b) --- Normalizing transformation --- p.41 / Chapter 3.3.4) --- Summary of 8-point algorithm --- p.41 / Chapter 3.4) --- Estimation of Object Position by Decomposition of Essential Matrix --- p.43 / Chapter 3.4.1) --- Algorithm Derivation --- p.43 / Chapter 3.4.2) --- Algorithm Outline --- p.46 / Chapter 3.5) --- Noise Sensitivity --- p.48 / Chapter 3.5.1) --- Rotation vector of model --- p.48 / Chapter 3.5.2) --- The projection of rotated model --- p.49 / Chapter 3.5.3) --- Noisy image --- p.51 / Chapter 3.5.4) --- Summary --- p.51 / Chapter 4) --- Pose Estimation --- p.54 / Chapter 4.1) --- Linear Method --- p.55 / Chapter 4.1.1) --- Theory --- p.55 / Chapter 4.1.2) --- Normalization --- p.57 / Chapter 4.1.3) --- Experimental Results --- p.58 / Chapter a) --- Synthesized image by linear method without normalization --- p.58 / Chapter b) --- Performance between linear method with and without normalization --- p.60 / Chapter c) --- Performance of linear method under quantization noise with different transformation components --- p.62 / Chapter d) --- Performance of normalized case without transformation in z- component --- p.63 / Chapter 4.1.4) --- Summary --- p.64 / Chapter 4.2) --- Two Stage Algorithm --- p.66 / Chapter 4.2.1) --- Introduction --- p.66 / Chapter 4.2.2) --- The Two Stage Algorithm --- p.67 / Chapter a) --- Stage 1 (Iterative Method) --- p.68 / Chapter b) --- Stage 2 ( Non-linear Optimization) --- p.71 / Chapter 4.2.3) --- Summary of the Two Stage Algorithm --- p.72 / Chapter 4.2.4) --- Experimental Results --- p.72 / Chapter 4.2.5) --- Summary --- p.80 / Chapter 5) --- Facial Motion Estimation and Synthesis --- p.81 / Chapter 5.1) --- Facial Expression based on face muscles --- p.83 / Chapter 5.1.1) --- Review of Action Unit Approach --- p.83 / Chapter 5.1.2) --- Distribution of Motion Unit --- p.85 / Chapter 5.1.3) --- Algorithm --- p.89 / Chapter a) --- For Unidirectional Motion Unit --- p.89 / Chapter b) --- For Circular Motion Unit (eyes) --- p.90 / Chapter c) --- For Another Circular Motion Unit (mouth) --- p.90 / Chapter 5.1.4) --- Experimental Results --- p.91 / Chapter 5.1.5) --- Summary --- p.95 / Chapter 5.2) --- Detection of Facial Expression by Muscle-based Approach --- p.96 / Chapter 5.2.1) --- Theory --- p.96 / Chapter 5.2.2) --- Algorithm --- p.97 / Chapter a) --- For Sheet Muscle --- p.97 / Chapter b) --- For Circular Muscle --- p.98 / Chapter c) --- For Mouth Muscle --- p.99 / Chapter 5.2.3) --- Steps of Algorithm --- p.100 / Chapter 5.2.4) --- Experimental Results --- p.101 / Chapter 5.2.5) --- Summary --- p.103 / Chapter 6) --- Conclusion --- p.104 / Chapter 6.1) --- WFM fitting --- p.104 / Chapter 6.2) --- Pose Estimation --- p.105 / Chapter 6.3) --- Facial Estimation and Synthesis --- p.106 / Chapter 6.4) --- Discussion on Future Improvements --- p.107 / Chapter 6.4.1) --- WFM Fitting --- p.107 / Chapter 6.4.2) --- Pose Estimation --- p.109 / Chapter 6.4.3) --- Facial Motion Estimation and Synthesis --- p.110 / Chapter 7) --- Appendix --- p.111 / Chapter 7.1) --- Newton's Method or Newton-Raphson Method --- p.111 / Chapter 7.2) --- H.261 --- p.113 / Chapter 7.3) --- 3D Measurement --- p.114 / Bibliography --- p.116

Kwong-Wai, Chen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 114-118). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgments --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Volumetric Data --- p.1 / Chapter 1.2 --- Volume Visualization --- p.4 / Chapter 1.3 --- Thesis Contributions --- p.5 / Chapter 1.4 --- Thesis Outline --- p.6 / Chapter I --- Multi-body Surface Extraction --- p.8 / Chapter 2 --- Isosurface Extraction --- p.9 / Chapter 2.1 --- Previous Works --- p.10 / Chapter 2.1.1 --- Marching Cubes --- p.10 / Chapter 2.1.2 --- Skeleton Climbing --- p.12 / Chapter 2.1.3 --- Adaptive Skeleton Climbing --- p.14 / Chapter 2.2 --- Motivation --- p.17 / Chapter 3 --- Multi-body Surface Extraction --- p.19 / Chapter 3.1 --- Multi-body Surface --- p.19 / Chapter 3.2 --- Building 0-skeleton --- p.21 / Chapter 3.3 --- Building 1-skeleton --- p.23 / Chapter 3.3.1 --- Non-binary Faces --- p.24 / Chapter 3.3.2 --- Non-binary Cubes --- p.30 / Chapter 3.4 --- General Scheme for Messy Cubes --- p.33 / Chapter 3.4.1 --- Graph Reduction --- p.34 / Chapter 3.4.2 --- Position of the Tetrapoints --- p.36 / Chapter 3.5 --- Triangular Mesh Generation --- p.37 / Chapter 3.5.1 --- Generating the Edge Loops --- p.38 / Chapter 3.5.2 --- Triangulating the Edge Loops --- p.41 / Chapter 3.5.3 --- Incorporating with Adaptive Skeleton Climbing --- p.43 / Chapter 3.6 --- Implementation and Results --- p.45 / Chapter II --- Haptic Rendering of Volumetric Data --- p.60 / Chapter 4 --- Introduction to Haptics --- p.61 / Chapter 4.1 --- Terminology --- p.62 / Chapter 4.2 --- Haptic Rendering Process --- p.63 / Chapter 4.2.1 --- The Overall Process --- p.64 / Chapter 4.2.2 --- Force Profile --- p.65 / Chapter 4.2.3 --- Decoupling Processes --- p.66 / Chapter 4.3 --- The PHANToM´ёØ Haptic Interface --- p.67 / Chapter 4.4 --- Research Goals --- p.69 / Chapter 5 --- Haptic Rendering of Geometric Models --- p.70 / Chapter 5.1 --- Penalty Based Methods --- p.71 / Chapter 5.1.1 --- Vector Fields for Solid Objects --- p.71 / Chapter 5.1.2 --- Drawbacks of Penalty Based Methods --- p.72 / Chapter 5.2 --- Constraint Based Methods --- p.73 / Chapter 5.2.1 --- Virtual Haptic Interface Point --- p.73 / Chapter 5.2.2 --- The Constraints --- p.74 / Chapter 5.2.3 --- Location Computation --- p.78 / Chapter 5.2.4 --- Force Shading --- p.79 / Chapter 5.2.5 --- Adding Surface Properties --- p.80 / Chapter 6 --- Haptic Rendering of Volumetric Data --- p.83 / Chapter 6.1 --- Volume Haptization --- p.84 / Chapter 6.2 --- Isosurface Haptic Rendering --- p.86 / Chapter 6.3 --- Intermediate Representation Approach --- p.89 / Chapter 6.3.1 --- Introduction --- p.89 / Chapter 6.3.2 --- Intermediate Virtual Plane --- p.90 / Chapter 6.3.3 --- Updating Virtual Plane --- p.92 / Chapter 6.3.4 --- Preventing Force Discontinuity Artifacts --- p.93 / Chapter 6.3.5 --- Experiments and Results --- p.94 / Chapter 7 --- Conclusions and Future Research Directions --- p.98 / Chapter 7.1 --- Conclusions --- p.98 / Chapter 7.2 --- Future Research Directions --- p.99 / Chapter A --- Two Proofs of Multi-body Surface Extraction Algorithm --- p.101 / Chapter A.1 --- Graph Terminology and Theorems --- p.101 / Chapter A.2 --- Occurrence of Tripoints in Negative-Positive Pairs --- p.103 / Chapter A.3 --- Validity of the General Scheme --- p.103 / Chapter B --- An Example of Multi-body Surface Extraction Algorithm --- p.105 / Chapter B.1 --- Step 1: Building 0-Skeleton --- p.105 / Chapter B.2 --- Step 2: Building 1-Skeleton --- p.106 / Chapter B.2.1 --- Step 2a: Building 1-Skeleton and Tripoints on Cube Faces --- p.106 / Chapter B.2.2 --- Step 2b: Adding Tetrapoints and Tri-edges inside Cube --- p.106 / Chapter B.3 --- Step 3: Constructing Edge Loops and Triangulating --- p.109 / Bibliography --- p.114

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 Yu-Hang Siu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 74-80). / Abstract also in Chinese. / Abstract --- p.iii / Acknowledgements --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Volume Visualization --- p.2 / Chapter 1.2 --- Virtual Environment --- p.11 / Chapter 1.3 --- Approach --- p.12 / Chapter 1.4 --- Thesis Overview --- p.13 / Chapter 2 --- Contour Extraction --- p.15 / Chapter 2.1 --- Concept of Intelligent Scissors --- p.16 / Chapter 2.2 --- Dijkstra's Algorithm --- p.18 / Chapter 2.3 --- Cost Function --- p.20 / Chapter 2.4 --- Summary --- p.23 / Chapter 3 --- Volume Cutting --- p.24 / Chapter 3.1 --- Basic idea of the algorithm --- p.25 / Chapter 3.2 --- Intelligent Scissors on Surface Mesh --- p.27 / Chapter 3.3 --- Internal Cutting Surface --- p.29 / Chapter 3.4 --- Summary --- p.34 / Chapter 4 --- Three-dimensional Intelligent Scissors --- p.35 / Chapter 4.1 --- 3D Graph Construction --- p.36 / Chapter 4.2 --- Cost Function --- p.40 / Chapter 4.3 --- Applications --- p.42 / Chapter 4.3.1 --- Surface Extraction --- p.42 / Chapter 4.3.2 --- Vessel Tracking --- p.47 / Chapter 4.4 --- Summary --- p.49 / Chapter 5 --- Implementations in a Virtual Environment --- p.52 / Chapter 5.1 --- Volume Cutting --- p.53 / Chapter 5.2 --- Surface Extraction --- p.56 / Chapter 5.3 --- Vessel Tracking --- p.59 / Chapter 5.4 --- Summary --- p.64 / Chapter 6 --- Conclusions --- p.68 / Chapter 6.1 --- Summary of Results --- p.68 / Chapter 6.2 --- Future Directions --- p.70 / Chapter A --- Performance of Dijkstra's Shortest Path Algorithm --- p.72 / Chapter B --- IsoRegion Construction --- p.73

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

by Xiang Shao hua. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 233-[248]). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

by Fung Wai Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 146-151). / Acknowledgements --- p.viii / Abstract --- p.1 / Chapter 1 --- Overview --- p.3 / Chapter 1.1 --- Tactile Sensing in Human and Robot --- p.4 / Chapter 1.1.1 --- Human Hands and Robotic Hands --- p.4 / Chapter 1.1.2 --- Mechanoreceptors in skin and Tactile Sensor Arrays --- p.7 / Chapter 1.2 --- Motivation --- p.12 / Chapter 1.3 --- Objectives --- p.13 / Chapter 1.4 --- Related Work --- p.14 / Chapter 1.4.1 --- Using Vision Alone --- p.15 / Chapter 1.4.2 --- Integration of Vision and Touch --- p.15 / Chapter 1.4.3 --- Using Touch Sensing Alone --- p.17 / Chapter 1.4.3.1 --- Ronald S. Fearing's Work --- p.18 / Chapter 1.4.3.2 --- Peter K. Allen's Work --- p.22 / Chapter 1.5 --- Outline --- p.26 / Chapter 2 --- Geometric Models --- p.27 / Chapter 2.1 --- Introduction --- p.27 / Chapter 2.2 --- Superquadrics --- p.27 / Chapter 2.2.1 --- 2D Superquadrics --- p.27 / Chapter 2.2.2 --- 3D Superquadrics --- p.29 / Chapter 2.3 --- Model Recovery of Superquadric Models --- p.31 / Chapter 2.3.1 --- Problem Formulation --- p.31 / Chapter 2.3.2 --- Least Squares Optimization --- p.33 / Chapter 2.4 --- Free-Form Deformations --- p.34 / Chapter 2.4.1 --- Bernstein Basis --- p.36 / Chapter 2.4.2 --- B-Spline Basis --- p.38 / Chapter 2.5 --- Other Geometric Models --- p.41 / Chapter 2.5.1 --- Generalized Cylinders --- p.41 / Chapter 2.5.2 --- Hyperquadrics --- p.42 / Chapter 2.5.3 --- Polyhedral Models --- p.44 / Chapter 2.5.4 --- Function Representation --- p.45 / Chapter 3 --- Sensing Strategy --- p.54 / Chapter 3.1 --- Introduction --- p.54 / Chapter 3.2 --- Sensing Algorithm --- p.55 / Chapter 3.2.1 --- Assumption of objects --- p.55 / Chapter 3.2.2 --- Haptic Exploration Procedures --- p.56 / Chapter 3.3 --- Contour Tracing --- p.58 / Chapter 3.4 --- Tactile Sensor Data Preprocessing --- p.59 / Chapter 3.4.1 --- Data Transformation and Sensor Calibration --- p.60 / Chapter 3.4.2 --- Noise Filtering --- p.61 / Chapter 3.5 --- Curvature Determination --- p.64 / Chapter 3.6 --- Step Size Determination --- p.73 / Chapter 4 --- 3D Shape Reconstruction --- p.80 / Chapter 4.1 --- Introduction --- p.80 / Chapter 4.2 --- Correspondence Problem --- p.81 / Chapter 4.2.1 --- Affine Invariance Property of B-splines --- p.84 / Chapter 4.2.2 --- Point Inversion Problem --- p.87 / Chapter 4.3 --- Parameter Triple Interpolation --- p.91 / Chapter 4.4 --- 3D Object Shape Reconstruction --- p.94 / Chapter 4.4.1 --- Heuristic Approach --- p.94 / Chapter 4.4.2 --- Closed Contour Recovery --- p.97 / Chapter 4.4.3 --- Control Lattice Recovery --- p.102 / Chapter 5 --- Implementation --- p.105 / Chapter 5.1 --- Introduction --- p.105 / Chapter 5.2 --- Implementation Tool - MATLAB --- p.105 / Chapter 5.2.1 --- Optimization Toolbox --- p.107 / Chapter 5.2.2 --- Splines Toolbox --- p.108 / Chapter 5.3 --- Geometric Model Implementation --- p.109 / Chapter 5.3.1 --- FFD Examples --- p.111 / Chapter 5.4 --- Shape Reconstruction Implementation --- p.112 / Chapter 5.5 --- 3D Model Reconstruction Examples --- p.120 / Chapter 5.5.1 --- Example 1 --- p.120 / Chapter 5.5.2 --- Example 2 --- p.121 / Chapter 6 --- Conclusion --- p.128 / Chapter 6.1 --- Future Work --- p.129 / Appendix --- p.133 / Bibliography --- p.146

Cheung Man-Tai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 81-84). / Abstracts in English and Chinese. / List of Figures --- p.vi / List of Tables --- p.viii / Chapter Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Background --- p.1 / Chapter 1.1.1. --- Parallax --- p.2 / Chapter 1.2. --- Literature Review --- p.3 / Chapter 1.3. --- Research Objective --- p.6 / Chapter 1.4. --- Organization of Thesis --- p.6 / Chapter Chapter 2. --- The 3-Image Algorithm --- p.1 / Chapter 2.1. --- Projective Reconstruction --- p.10 / Chapter 2.2. --- Epipolar Geometry and Fundamental Matrix --- p.11 / Chapter 2.3. --- Determine the Projective Mapping --- p.12 / Chapter 2.3.1. --- Conditions for Initial Matches --- p.13 / Chapter 2.3.2. --- Obtaining the Feature Correspondence --- p.17 / Chapter 2.4. --- Registering Pixel Element --- p.21 / Chapter 2.4.1. --- Single Homography Approach --- p.22 / Chapter 2.4.2. --- Multiple Homography Approach --- p.23 / Chapter 2.4.3. --- Triangular Patches Clustering --- p.24 / Chapter 2.4.3.1. --- Delaunay Triangulation --- p.25 / Chapter 2.5. --- Mosaic Construction --- p.29 / Chapter Chapter 3. --- The n-Image Algorithm --- p.31 / Chapter Chapter 4. --- The Uneven-Sampling-Rate n-Image Algorithm --- p.34 / Chapter 4.1. --- Varying the Reference-Target Images Separation --- p.35 / Chapter 4.2. --- Varying the Target-Intermediate Images Separation --- p.38 / Chapter Chapter 5. --- Experiments --- p.43 / Chapter 5.1. --- Experimental Setup --- p.43 / Chapter 5.1.1. --- Measuring the Performance --- p.43 / Chapter 5.2. --- Experiments on the 3-Image Algorithm --- p.44 / Chapter 5.2.1. --- Planar Scene --- p.44 / Chapter 5.2.2. --- Comparison between a Global Parametric Transformation and the 3-Image Algorithm --- p.46 / Chapter 5.2.3. --- Generic Scene --- p.49 / Chapter 5.2.4. --- The Triangular Patches Clustering against the Multiple Homography Approach --- p.52 / Chapter 5.3. --- Experiments on the n-Image Algorithm --- p.56 / Chapter 5.3.1. --- Initial Experiment on the n-Image Algorithm --- p.56 / Chapter 5.3.2. --- Another Experiment on the n-Image Algorithm --- p.58 / Chapter 5.3.3. --- the n-Image Algorithm over a Longer Image Stream --- p.61 / Chapter 5.4. --- Experiments on the Uneven-Sampling-Rate n-Image Algorithm --- p.65 / Chapter 5.4.1. --- Varying Reference-Target Images Separation --- p.65 / Chapter 5.4.2. --- Varying Target-Intermediate Images Separation --- p.69 / Chapter 5.4.3. --- Comparing the Uneven-Sampling-Rate n-Image Algorithm and Global Transformation Method --- p.73 / Chapter Chapter 6. --- Conclusion and Discussion --- p.76 / Bibliography --- p.81