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.

The purpose of the X-Form package is for use in the classroom as an instructional aid for teaching the algorithms and data used to represent, transform, and display objects in three-dimensional space. The program provides various areas containing graphic and textual representations of the data structures used in the creation and transformation of a three-dimensional object. Another area, containing the menu items, serves as an interface to the available demonstration items. These items include various object and viewing parameters which can be input by the user. The user then sees the result of the entered data, including animated graphics and changes in data structures. The graphic areas show the object at various stages of development, so that the user can understand the progression of the required manipulations. The textual representations are also designed to assist the user in understanding how useful information is derived from entered data. Through testing and statistical analysis, XForm has proven to be an effective tool for instruction of three-dimensional graphics. / Department of Computer Science

Early image understanding seeks to derive analytic representations from image data. This thesis presents steps towards this goal for three-dimensional imagery by focusing on the inference of trace points (points belonging to surfaces), and the estimation of associated differential structure given by the principal curvature and direction fields over smooth surfaces. Computation of these fields is posed as the determination of a cross section through the bundle of curvature frames over the estimated trace. Algorithm robustness and the stability of results are essential for analysis of real images; to this end, I present a functional minimization algorithm where the principal direction cross section meets appropriate criteria for a minimum, and develop an implementation as an iterative constraint satisfaction procedure based on local surface smoothness properties. For shape description and eventual object recognition, the exact recovery of local structure everywhere is less important than the identification of singular surface points which prove stable to noise and small surface perturbations, in particular, the umbilic points of surfaces. Such points are computed naturally from the estimated local surface structure embodied in the principal direction cross section of the frame bundle. Examples of the recovery of local structure are presented for synthetic images degraded by noise and for clinical magnetic resonance images.