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 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

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

The human face is a fascinating, but extremely complex object; the research project described is concerned with the computer generation and animation of faces. However, the age old captivation with the face transforms into a major obstacle when creating synthetic faces. The face and head are the most visible attributes of a person. We master the skills of recognising faces and interpreting facial movement at a very early age. As a result, we are likely to notice the smallest deviation from our concept of how a face should appear and behave. Computer animation in general, is often perceived to be ``wooden' and very ``rigid'; the aim is therefore to provide facilities for the generation of believable faces and convincing facial movement. The major issues addressed within the project concern the modelling of a large variety of faces and their animation. Computer modelling of arbitrary faces is an area that has received relatively little attention in comparison with the animation of faces. Another problem that has been considered is that of providing the user with adequate and effective control over the modelling and animation of the face. The Facial Animation, Construction and Editing System or FACES was conceived as a system for investigating these issues. A promising approach is to look a little deeper than the surface of the skin. A three-layer anatomical model of the head, which incorporates bone, muscle, skin and surface features, has been developed. As well as serving as a foundation which integrates all the facilities available within FACES, the advantage of the model is that it allows differing strategies to be used for modelling and animation. FACES is an interactive system, which helps with both the generation and animation of faces, while hiding the structural complexities of the face from the user. The software consists of four sub-systems; CONSTRUCT and MODIFY cater for modelling functionality, while ANIMATE allows animation sequences to be generated and RENDER provides for shading and motion evaluation.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Computer Science.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERONAUTICS. / Includes bibliographical references. / by Michael Derryck Turner. / M.S.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Architecture. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH. / Bibliography: leaf 35. / M.S.

Both man-made or natural objects contain repeated geometric elements that can be interpreted as primitive shapes. Plants, trees, living organisms or even crystals, showcase primitives that repeat themselves. Primitives are also commonly found in man-made environments because architects tend to reuse the same patterns over a building and typically employ simple shapes, such as rectangular windows and doors. During my PhD I studied geometric primitives from three points of view: their composition, simulation and autonomous discovery. In the first part I present a method to reverse-engineer the function by which some primitives are combined. Our system is based on a composition function template that is represented by a parametric surface. The parametric surface is deformed via a non-rigid alignment of a surface that, once converged, represents the desired operator. This enables the interactive modeling of operators via a simple sketch, solving a major usability gap of composition modeling. In the second part I introduce the use of a novel primitive for real-time physics simulations. This primitive is suitable to efficiently model volume-preserving deformations of rods but also of more complex structures such as muscles. One of the core advantages of our approach is that our primitive can serve as a unified representation to do collision detection, simulation, and surface skinning. In the third part I present an unsupervised deep learning framework to learn and detect primitives. In a signal containing a repetition of elements, the method is able to automatically identify the structure of these elements (i.e. primitives) with minimal supervision. In order to train the network that contains a non-differentiable operation, a novel multi-step training process is presented. / Graduate

Fok Ka Ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 96-101). / Abstracts in English and Chinese. / Abstract --- p.1 / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Evolutionary Computation --- p.1 / Chapter 1.2 --- Graphics Processing Unit --- p.2 / Chapter 1.3 --- Objective --- p.3 / Chapter 1.4 --- Contribution --- p.4 / Chapter 1.5 --- Thesis Organization --- p.4 / Chapter 2 --- Evolutionary Computation --- p.6 / Chapter 2.1 --- Introduction --- p.6 / Chapter 2.2 --- General Framework --- p.7 / Chapter 2.3 --- Features of Evolutionary Algorithm --- p.8 / Chapter 2.3.1 --- Widely Applicable --- p.8 / Chapter 2.3.2 --- Parallelism --- p.9 / Chapter 2.3.3 --- Robust to Change --- p.9 / Chapter 2.4 --- Parallel and Distributed Evolutionary Algorithm --- p.9 / Chapter 2.4.1 --- Global Parallel Evolutionary Algorithms --- p.10 / Chapter 2.4.2 --- Fine-Grained Evolutionary Algorithms --- p.11 / Chapter 2.4.3 --- Island Distributed Evolutionary Algorithms --- p.12 / Chapter 2.5 --- Summary --- p.14 / Chapter 3 --- Graphics Processing Unit --- p.15 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.2 --- History of GPU --- p.16 / Chapter 3.2.1 --- First-Generation GPUs --- p.16 / Chapter 3.2.2 --- Second-Generation GPUs --- p.17 / Chapter 3.2.3 --- Third-Generation GPUs --- p.17 / Chapter 3.2.4 --- Fourth-Generation GPUs --- p.17 / Chapter 3.3 --- The Graphics Pipelining --- p.18 / Chapter 3.3.1 --- Standard Graphics Pipeline --- p.18 / Chapter 3.3.2 --- Programmable Graphics Pipeline --- p.18 / Chapter 3.3.3 --- Fragment Processors for Scientific Computation --- p.21 / Chapter 3.4 --- GPU-CPU Analogy --- p.23 / Chapter 3.4.1 --- Memory Architecture --- p.23 / Chapter 3.4.2 --- Processing Model --- p.24 / Chapter 3.5 --- Limitation of GPU --- p.24 / Chapter 3.5.1 --- Limited Input and Output --- p.24 / Chapter 3.5.2 --- Slow Data Readback --- p.24 / Chapter 3.5.3 --- No Random Number Generator --- p.25 / Chapter 3.6 --- Summary --- p.25 / Chapter 4 --- Evolutionary Programming on GPU --- p.26 / Chapter 4.1 --- Introduction --- p.26 / Chapter 4.2 --- Evolutionary Programming --- p.26 / Chapter 4.3 --- Data Organization --- p.29 / Chapter 4.4 --- Fitness Evaluation --- p.31 / Chapter 4.4.1 --- Introduction --- p.31 / Chapter 4.4.2 --- Different Forms of Fitness Function --- p.32 / Chapter 4.4.3 --- Parallel Fitness Function Evaluation using GPU --- p.33 / Chapter 4.5 --- Mutation --- p.34 / Chapter 4.5.1 --- Introduction --- p.34 / Chapter 4.5.2 --- Self Adaptive Mutation Operators --- p.36 / Chapter 4.5.3 --- Mutation on GPU --- p.37 / Chapter 4.6 --- Selection for Replacement --- p.39 / Chapter 4.6.1 --- Introduction --- p.39 / Chapter 4.6.2 --- Classification of Selection Operator --- p.39 / Chapter 4.6.3 --- q -Tournament Selection --- p.40 / Chapter 4.6.4 --- Median Searching --- p.41 / Chapter 4.6.5 --- Minimizing Data Transfer --- p.43 / Chapter 4.7 --- Experimental Results --- p.44 / Chapter 4.7.1 --- Visualization --- p.48 / Chapter 4.8 --- Summary --- p.49 / Chapter 5 --- Genetic Algorithm on GPU --- p.56 / Chapter 5.1 --- Introduction --- p.56 / Chapter 5.2 --- Canonical Genetic Algorithm --- p.57 / Chapter 5.2.1 --- Parent Selection --- p.57 / Chapter 5.2.2 --- Crossover and Mutation --- p.62 / Chapter 5.2.3 --- Replacement --- p.63 / Chapter 5.3 --- Experiment Results --- p.64 / Chapter 5.4 --- Summary --- p.66 / Chapter 6 --- Multi-Objective Genetic Algorithm --- p.70 / Chapter 6.1 --- Introduction --- p.70 / Chapter 6.2 --- Definitions --- p.71 / Chapter 6.2.1 --- General MOP --- p.71 / Chapter 6.2.2 --- Decision Variables --- p.71 / Chapter 6.2.3 --- Constraints --- p.71 / Chapter 6.2.4 --- Feasible Region --- p.72 / Chapter 6.2.5 --- Optimal Solution --- p.72 / Chapter 6.2.6 --- Pareto Optimum --- p.73 / Chapter 6.2.7 --- Pareto Front --- p.73 / Chapter 6.3 --- Multi-Objective Genetic Algorithm --- p.75 / Chapter 6.3.1 --- Ranking --- p.76 / Chapter 6.3.2 --- Fitness Scaling --- p.77 / Chapter 6.3.3 --- Diversity Preservation --- p.77 / Chapter 6.4 --- A Niched and Elitism Multi-Objective Genetic Algorithm on GPU --- p.79 / Chapter 6.4.1 --- Objective Values Evaluation --- p.80 / Chapter 6.4.2 --- Pairwise Pareto Dominance and Pairwise Distance --- p.81 / Chapter 6.4.3 --- Fitness Assignment --- p.85 / Chapter 6.4.4 --- Embedded Archiving Replacement --- p.87 / Chapter 6.5 --- Experiment Result --- p.89 / Chapter 6.6 --- Summary --- p.90 / Chapter 7 --- Conclusion --- p.95 / Bibliography --- p.96