by Po-Ming Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 94-100). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Image Data --- p.2 / Chapter 1.2.1 --- Feature Detection --- p.2 / Chapter 1.3 --- Neural Networks --- p.4 / Chapter 1.4 --- Invariant Object Recognition --- p.5 / Chapter 1.5 --- Thesis Outline --- p.7 / Chapter 2 --- Feature Extraction --- p.8 / Chapter 2.1 --- Review of the Principle Component Analysis (PCA) Method --- p.9 / Chapter 2.1.1 --- Theory --- p.10 / Chapter 2.2 --- Covariance Operator --- p.13 / Chapter 2.3 --- Corner Extraction Method --- p.16 / Chapter 2.3.1 --- Corner Detection on the Surface of an Object --- p.16 / Chapter 2.3.2 --- Corner Detection at Boundary Region --- p.17 / Chapter 2.3.3 --- Steps in Corner Detection Process --- p.21 / Chapter 2.4 --- Experiment Results and Discussion --- p.23 / Chapter 2.4.1 --- Features Localization --- p.27 / Chapter 2.4.2 --- Preparing Feature Points for Matching Process --- p.32 / Chapter 2.5 --- Summary --- p.32 / Chapter 3 --- Invariant Graph Matching Using High-Order Hopfield Network --- p.36 / Chapter 3.1 --- Review of the Hopfield Network --- p.37 / Chapter 3.1.1 --- 3D Image Matching Algorithm --- p.40 / Chapter 3.1.2 --- Iteration Algorithm --- p.44 / Chapter 3.2 --- Third-order Hopfield Network --- p.45 / Chapter 3.3 --- Experimental Results --- p.49 / Chapter 3.4 --- Summary --- p.58 / Chapter 4 --- Hopfield Network for 2D and 3D Mirror-Symmetric Image Match- ing --- p.59 / Chapter 4.1 --- Introduction --- p.59 / Chapter 4.2 --- Geometric Symmetry --- p.60 / Chapter 4.3 --- Motivation --- p.62 / Chapter 4.4 --- Third-order Hopfield Network for Solving 2D Symmetry Problems --- p.66 / Chapter 4.5 --- Forth-order Hopfield Network for Solving 3D Symmetry Problem --- p.71 / Chapter 4.6 --- Experiment Results --- p.78 / Chapter 4.7 --- Summary --- p.88 / Chapter 5 --- Conclusion --- p.90 / Chapter 5.1 --- Results and Contributions --- p.90 / Chapter 5.2 --- Future Work --- p.92 / Bibliography --- p.94

Chou Yun-Sum, Bailey. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 89-92). / Abstracts in English and Chinese. / List of Symbol / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- The Goal: Euclidean Reconstruction --- p.1 / Chapter 1.2 --- Historical background --- p.2 / Chapter 1.3 --- Scope of the thesis --- p.2 / Chapter 1.4 --- Thesis Outline --- p.3 / Chapter Chapter 2 --- An introduction to stereo vision and 3D shape reconstruction / Chapter 2.1 --- Homogeneous Coordinates --- p.4 / Chapter 2.2 --- Camera Model / Chapter 2.2.1 --- Pinhole Camera Model --- p.5 / Chapter 2.3 --- Camera Calibration --- p.11 / Chapter 2.4 --- Geometry of Binocular System --- p.14 / Chapter 2.5 --- Stereo Matching --- p.15 / Chapter 2.5.1 --- Accuracy of Corresponding Point --- p.17 / Chapter 2.5.2 --- The Stereo Matching Approach --- p.18 / Chapter 2.5.2.1 --- Intensity-based stereo matching --- p.19 / Chapter 2.5.2.2 --- Feature-based stereo matching --- p.20 / Chapter 2.5.3 --- Matching Constraints --- p.20 / Chapter 2.6 --- 3D Reconstruction --- p.22 / Chapter 2.7 --- Recent development on self calibration --- p.24 / Chapter 2.8 --- Summary of the Chapter --- p.25 / Chapter Chapter 3 --- Camera Calibration / Chapter 3.1 --- Introduction --- p.26 / Chapter 3.2 --- Camera Self-calibration --- p.27 / Chapter 3.3 --- Self-calibration under general camera motion --- p.27 / Chapter 3.3.1 --- The absolute Conic Based Techniques --- p.28 / Chapter 3.3.2 --- A Stratified approach for self-calibration by Pollefeys --- p.33 / Chapter 3.3.3 --- Pollefeys self-calibration with Absolute Quadric --- p.34 / Chapter 3.3.4 --- Newsam's self-calibration with linear algorithm --- p.34 / Chapter 3.4 --- Camera Self-calibration under specially designed motion sequence / Chapter 3.4. 1 --- Hartley's self-calibration by pure rotations --- p.35 / Chapter 3.4.1.1 --- Summary of the Algorithm / Chapter 3.4.2 --- Pollefeys self-calibration with variant focal length --- p.36 / Chapter 3.4.2.1 --- Summary of the Algorithm / Chapter 3.4.3 --- Faugeras self-calibration of a 1D Projective Camera --- p.38 / Chapter 3.5 --- Summary of the Chapter --- p.39 / Chapter Chapter 4 --- Self-calibration under Planar motions / Chapter 4.1 --- Introduction --- p.40 / Chapter 4.2 --- 1D Projective Camera Self-calibration --- p.41 / Chapter 4.2.1 --- 1-D camera model --- p.42 / Chapter 4.2.2 --- 1-D Projective Camera Self-calibration Algorithms --- p.44 / Chapter 4.2.3 --- Planar motion detection --- p.45 / Chapter 4.2.4 --- Self-calibration under horizontal planar motions --- p.46 / Chapter 4.2.5 --- Self-calibration under three different planar motions --- p.47 / Chapter 4.2.6 --- Result analysis on self-calibration Experiments --- p.49 / Chapter 4.3 --- Essential Matrix and Triangulation --- p.51 / Chapter 4.4 --- Merge of Partial 3D models --- p.51 / Chapter 4.5 --- Summary of the Reconstruction Algorithms --- p.53 / Chapter 4.6 --- Experimental Results / Chapter 4.6.1 --- Experiment 1 : A Simulated Box --- p.54 / Chapter 4.6.2 --- Experiment 2 : A Real Building --- p.57 / Chapter 4.6.3 --- Experiment 3 : A Sun Flower --- p.58 / Chapter 4.7 --- Conclusion --- p.59 / Chapter Chapter 5 --- Building Reconstruction using a linear camera self- calibration technique / Chapter 5.1 --- Introduction --- p.60 / Chapter 5.2 --- Metric Reconstruction from Partially Calibrated image / Chapter 5.2.1 --- Partially Calibrated Camera --- p.62 / Chapter 5.2.2 --- Optimal Computation of Fundamental Matrix (F) --- p.63 / Chapter 5.2.3 --- Linearly Recovering Two Focal Lengths from F --- p.64 / Chapter 5.2.4 --- Essential Matrix and Triangulation --- p.66 / Chapter 5.3 --- Experiments and Discussions --- p.67 / Chapter 5.4 --- Conclusion --- p.71 / Chapter Chapter 6 --- Refine the basic model with detail depth information by a Model-Based Stereo technique / Chapter 6.1 --- Introduction --- p.72 / Chapter 6.2 --- Model Based Epipolar Geometry / Chapter 6.2.1 --- Overview --- p.74 / Chapter 6.2.2 --- Warped offset image preparation --- p.76 / Chapter 6.2.3 --- Epipolar line calculation --- p.78 / Chapter 6.2.4 --- Actual corresponding point finding by stereo matching --- p.80 / Chapter 6.2.5 --- Actual 3D point generated by Triangulation --- p.80 / Chapter 6.3 --- Summary of the Algorithms --- p.81 / Chapter 6.4 --- Experiments and discussions --- p.83 / Chapter 6.5 --- Conclusion --- p.85 / Chapter Chapter 7 --- Conclusions / Chapter 7.1 --- Summary --- p.86 / Chapter 7.2 --- Future Work --- p.88 / BIBLIOGRAPHY --- p.89

Leung, Man Chuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 81-88). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objective --- p.1 / Chapter 1.2 --- Contribution --- p.3 / Chapter 1.3 --- Organization of the Thesis --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Static Projector and Screen Systems --- p.6 / Chapter 2.3 --- Dynamic Projector or Screen Systems --- p.9 / Chapter 2.3.1 --- Movable Projector Systems --- p.10 / Chapter 2.3.2 --- Dynamic Screen Systems --- p.11 / Chapter 2.4 --- Summary --- p.17 / Chapter 3 --- System Overview --- p.18 / Chapter 3.1 --- System Design --- p.18 / Chapter 3.2 --- Our Approach --- p.18 / Chapter 3.2.1 --- Offline Projector Camera Calibration --- p.20 / Chapter 3.2.2 --- Quadrangle Detection and Tracking --- p.20 / Chapter 3.2.3 --- Projection --- p.22 / Chapter 3.3 --- Extension --- p.22 / Chapter 4 --- Projector-Camera Pair Calibration --- p.23 / Chapter 4.1 --- Introduction --- p.23 / Chapter 4.2 --- Projective Geometry of a Projector --- p.25 / Chapter 4.3 --- Calibration Method --- p.27 / Chapter 5 --- Quadrangle Detection and Tracking --- p.31 / Chapter 5.1 --- Introduction --- p.31 / Chapter 5.2 --- Line Feature Extraction --- p.33 / Chapter 5.3 --- Automatic Quadrangle Detection --- p.33 / Chapter 5.4 --- Real-time Quadrangle Tracking --- p.36 / Chapter 5.4.1 --- State Dynamic Model --- p.39 / Chapter 5.4.2 --- Observation Model --- p.39 / Chapter 5.5 --- Tracking Lose Strategy --- p.41 / Chapter 5.5.1 --- Determination of Tracking Failure --- p.42 / Chapter 5.6 --- Recover from Tracking Failure --- p.43 / Chapter 6 --- Projection onto the Cardboard --- p.44 / Chapter 7 --- Implementation and Experiment Results --- p.47 / Chapter 7.1 --- Introduction --- p.47 / Chapter 7.2 --- Projector-Camera Pair Calibration --- p.49 / Chapter 7.3 --- Quadrangle Detection and Tracking --- p.51 / Chapter 7.3.1 --- Experiment 1 - Tracking precision and robustness against occlusion --- p.51 / Chapter 7.3.2 --- Experiment 2 - Robustness against dense clutter --- p.52 / Chapter 7.3.3 --- Experiment 3 - Tracking of a paper with printed content --- p.53 / Chapter 7.3.4 --- Experiment 4 - Moving camera --- p.53 / Chapter 7.3.5 --- Processing Time --- p.55 / Chapter 7.4 --- Projection Performance --- p.57 / Chapter 7.4.1 --- Projection Precision --- p.57 / Chapter 7.4.2 --- Projection Latency --- p.58 / Chapter 8 --- Limitations and Discussions --- p.61 / Chapter 8.1 --- Limitation on Projection Resolution --- p.61 / Chapter 8.2 --- Limitation on Depth of Field --- p.62 / Chapter 8.3 --- Tracking Stability and Processing Time --- p.62 / Chapter 8.4 --- Handling Projected Light --- p.63 / Chapter 8.5 --- Possible Extensions --- p.63 / Chapter 9 --- View Dependent Projection and Application --- p.65 / Chapter 9.1 --- View Dependent Projection --- p.65 / Chapter 9.2 --- Head Pose Tracking --- p.67 / Chapter 9.3 --- Application - Hand-held 3D Model Viewer --- p.68 / Chapter 9.3.1 --- Introduction --- p.68 / Chapter 9.3.2 --- Implementation Detail --- p.69 / Chapter 9.3.3 --- Experiment Results --- p.73 / Chapter 9.3.4 --- Discussions --- p.73 / Chapter 9.4 --- Summary --- p.75 / Chapter 10 --- Conclusions --- p.77 / A Pose Estimation of Cardboard --- p.79 / Bibliography --- p.81

Li, Sai Man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 91-96). / Abstracts in English and Chinese. / Abstract --- p.2 / List of Figures --- p.6 / Acknowledgement --- p.10 / Chapter Chapter 1 --- Introduction --- p.11 / Chapter 1.1 --- Problem definition --- p.11 / Chapter 1.2. --- Proposed algorithm --- p.12 / Chapter 1.3. --- Main features --- p.14 / Chapter Chapter 2 --- Literature Review --- p.16 / Chapter 2.1 --- Local Feature Matching techniques --- p.16 / Chapter 2.2. --- Global Iterative alignment techniques --- p.19 / Chapter 2.3 --- Other Approaches --- p.20 / Chapter Chapter 3 --- Correspondence Matching --- p.21 / Chapter 3.1 --- Fundamental Techniques --- p.24 / Chapter 3.1.1 --- Geodesic Distance Approximation --- p.24 / Chapter 3.1.1.1 --- Dijkstra ´ةs algorithm --- p.25 / Chapter 3.1.1.2 --- Wavefront Propagation --- p.26 / Chapter 3.1.2 --- Farthest Point Sampling --- p.27 / Chapter 3.1.3 --- Curvature Estimation --- p.29 / Chapter 3.1.4 --- Radial Basis Function (RBF) --- p.32 / Chapter 3.1.5 --- Multi-dimensional Scaling (MDS) --- p.35 / Chapter 3.1.5.1 --- Classical MDS --- p.35 / Chapter 3.1.5.2 --- Fast MDS --- p.38 / Chapter 3.2 --- Matching Processes --- p.40 / Chapter 3.2.1 --- Posture Alignment --- p.42 / Chapter 3.2.1.1 --- Sign Flip Correction --- p.43 / Chapter 3.2.1.2 --- Input model Alignment --- p.49 / Chapter 3.2.2 --- Surface Fitting --- p.52 / Chapter 3.2.2.1 --- Optimizing Surface Fitness --- p.54 / Chapter 3.2.2.2 --- Optimizing Surface Smoothness --- p.56 / Chapter 3.2.3 --- Feature Matching Refinement --- p.59 / Chapter 3.2.3.1 --- Feature descriptor --- p.61 / Chapter 3.2.3.3 --- Feature Descriptor matching --- p.63 / Chapter Chapter 4 --- Experimental Result --- p.66 / Chapter 4.1 --- Result of the Fundamental Techniques --- p.66 / Chapter 4.1.1 --- Geodesic Distance Approximation --- p.67 / Chapter 4.1.2 --- Farthest Point Sampling (FPS) --- p.67 / Chapter 4.1.3 --- Radial Basis Function (RBF) --- p.69 / Chapter 4.1.4 --- Curvature Estimation --- p.70 / Chapter 4.1.5 --- Multi-Dimensional Scaling (MDS) --- p.71 / Chapter 4.2 --- Result of the Core Matching Processes --- p.73 / Chapter 4.2.1 --- Posture Alignment Step --- p.73 / Chapter 4.2.2 --- Surface Fitting Step --- p.78 / Chapter 4.2.3 --- Feature Matching Refinement --- p.82 / Chapter 4.2.4 --- Application of the proposed algorithm --- p.84 / Chapter 4.2.4.1 --- Design Automation in Garment Industry --- p.84 / Chapter 4.3 --- Analysis --- p.86 / Chapter 4.3.1 --- Performance --- p.86 / Chapter 4.3.2 --- Accuracy --- p.87 / Chapter 4.3.3 --- Approach Comparison --- p.88 / Chapter Chapter 5 --- Conclusion --- p.89 / Chapter 5.1 --- Strength and contributions --- p.89 / Chapter 5.2 --- Limitation and future works --- p.90 / References --- p.91

Jiang, Li. / "October 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 71-76). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Three Dimensional Integrated Circuit --- p.1 / Chapter 1.1.1 --- 3D ICs --- p.1 / Chapter 1.1.2 --- Manufacture --- p.3 / Chapter 1.2 --- Test Architecture Design and Optimization for SoCs --- p.4 / Chapter 1.2.1 --- Test Wrapper --- p.4 / Chapter 1.2.2 --- Test Access Mechanism --- p.6 / Chapter 1.2.3 --- Test Architecture Optimization and Test Scheduling --- p.7 / Chapter 1.3 --- Thesis Motivation and Organization --- p.9 / Chapter 2 --- On Test Time and Routing Cost --- p.12 / Chapter 2.1 --- Introduction --- p.12 / Chapter 2.2 --- Preliminaries and Motivation --- p.13 / Chapter 2.3 --- Problem Formulation --- p.17 / Chapter 2.3.1 --- Test Cost Model --- p.17 / Chapter 2.3.2 --- Routing Model --- p.17 / Chapter 2.3.3 --- Problem Definition --- p.19 / Chapter 2.4 --- Proposed Algorithm --- p.22 / Chapter 2.4.1 --- Outline of The Proposed Algorithm --- p.22 / Chapter 2.4.2 --- SA-Based Core Assignment --- p.24 / Chapter 2.4.3 --- Heuristic-Based TAM Width Allocation --- p.25 / Chapter 2.4.4 --- Fast routing Heuristic --- p.28 / Chapter 2.5 --- Experiments --- p.29 / Chapter 2.5.1 --- Experimental Setup --- p.29 / Chapter 2.5.2 --- Experimental Results --- p.31 / Chapter 2.6 --- Conclusion --- p.34 / Chapter 3 --- Pre-bond-Test-Pin Constrained Test Wire Sharing --- p.37 / Chapter 3.1 --- Introduction --- p.37 / Chapter 3.2 --- Preliminaries and Motivation --- p.38 / Chapter 3.2.1 --- Prior Work in SoC Testing --- p.38 / Chapter 3.2.2 --- Prior Work in Testing 3D ICs --- p.39 / Chapter 3.2.3 --- Test-Pin-Count Constraint in 3D IC Pre-Bond Testing --- p.40 / Chapter 3.2.4 --- Motivation --- p.41 / Chapter 3.3 --- Problem Formulation --- p.43 / Chapter 3.3.1 --- Test Architecture Design under Pre-Bond Test-Pin-Count Constraint --- p.44 / Chapter 3.3.2 --- Thermal-aware Test Scheduling for Post-Bond Test --- p.45 / Chapter 3.4 --- Layout-Driven Test Architecture Design and Optimization --- p.46 / Chapter 3.4.1 --- Scheme 1: TAM Wire Reuse with Fixed Test Architectures --- p.46 / Chapter 3.4.2 --- Scheme 2: TAM Wire Reuse with Flexible Pre-bond Test Architecture --- p.52 / Chapter 3.5 --- Thermal-Aware Test Scheduling for Post-Bond Test --- p.53 / Chapter 3.5.1 --- Thermal Cost Function --- p.54 / Chapter 3.5.2 --- Test Scheduling Algorithm --- p.55 / Chapter 3.6 --- Experimental Results --- p.56 / Chapter 3.6.1 --- Experimental Setup --- p.56 / Chapter 3.6.2 --- Results and Discussion --- p.58 / Chapter 3.7 --- Conclusion --- p.59 / Chapter 3.8 --- Acknowledgement --- p.60 / Chapter 4 --- Conclusion and Future Work --- p.69 / Bibliography --- p.70

3D design is being recognized widely as the next BIG thing in system integration. However, design and analysis tools for 3D are still in infancy stage. Power supply noise analysis is one of the critical aspects of a design. Hence, the area of noise analysis for 3D designs is a key area for future development. The following research presents a new parasitic RLC modeling technique for 3D chips containing TSVs as well as a novel optimization algorithm for power-ground network of a 3D chip with the aim of minimizing noise in the network. The following work also looks into an existing commercial IR drop analysis tool and presents a way to modify it with the aim of handling 3D designs containing TSVs.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 174-179). Also available via the Internet

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 202-221). Also available on the Internet.

Thesis (M. Sc.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (leaves 82-93). Also available via the World Wide Web.

Conventional stereoscopic displays present a pair of stereoscopic images on a single and fixed image plane decoupled with the vergence and accommodation responses of the viewer. In consequence, these displays lack the capability of correctly rendering focus cues (i.e. accommodation and retinal blur) and may induce the discrepancy between accommodation and convergence. A number of visual artifacts associated with incorrect focus cues in stereoscopic displays have been reported, limiting the applicability of these displays for demanding applications and daily usage.In this dissertation, methods and apparatus for generating addressable focus cues in conventional stereoscopic displays are proposed. Focus cues can be addressed throughout a volumetric space, either through dynamically varying the focal distance of a display enabled by an active optical element or by multiplexing a stack of 2-D image planes. Optimal depth-weighted fusing functions are developed to fuse a number of discrete image planes into a seamless volumetric space with continuous and near-correct focus cues similar to the real world counterparts.The optical design, driving methodology, and prototype implementation of the addressable focus displays are presented and discussed. Experimental results demonstrate continuously addressable focus cues from infinity to as close as the near eye distance. Experiments to further evaluate the depth perception in the display prototype are conducted. Preliminary results suggest that the perceived distance and accommodative response of the viewer match with the addressable accommodation cues rendered by the display, approximating the real-world viewing condition.