Thesis (Ph. D.)--University of Wisconsin--Madison, 1982. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 149-161).

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 93-99).

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains x, 80 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 75-80).

The problem of restoring an image distorted by a system consisting of a stochastic impulse response in conjuction with additive noise is investigated. The method of constrained least squares is extended to this problem, and leads to the development of a new technique based on the minimization of a weighted error function. Results obtained using the new method are compared with those obtained by constrained least squares, and by the Wiener filter and approximations thereof. It is found that the new technique, "Weighted Least Squares", gives superior results if the noise in the impulse response is comparable to or greater than the additive noise. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

No description available.

published_or_final_version / abstract / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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

Lee Kam Sum. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 97-101). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Video representation using panorama mosaic and 3D face model --- p.2 / Chapter 1.2 --- Mosaic-based Video Representation --- p.3 / Chapter 1.3 --- "3D Human Face modeling ," --- p.7 / Chapter 2 --- Background --- p.13 / Chapter 2.1 --- Video Representation using Mosaic Image --- p.13 / Chapter 2.1.1 --- Traditional Video Compression --- p.17 / Chapter 2.2 --- 3D Face model Reconstruction via Multiple Views --- p.19 / Chapter 2.2.1 --- Shape from Silhouettes --- p.19 / Chapter 2.2.2 --- Head and Face Model Reconstruction --- p.22 / Chapter 2.2.3 --- Reconstruction using Generic Model --- p.24 / Chapter 3 --- System Overview --- p.27 / Chapter 3.1 --- Panoramic Video Coding Process --- p.27 / Chapter 3.2 --- 3D Face model Reconstruction Process --- p.28 / Chapter 4 --- Panoramic Video Representation --- p.32 / Chapter 4.1 --- Mosaic Construction --- p.32 / Chapter 4.1.1 --- Cylindrical Panorama Mosaic --- p.32 / Chapter 4.1.2 --- Cylindrical Projection of Mosaic Image --- p.34 / Chapter 4.2 --- Foreground Segmentation and Registration --- p.37 / Chapter 4.2.1 --- Segmentation Using Panorama Mosaic --- p.37 / Chapter 4.2.2 --- Determination of Background by Local Processing --- p.38 / Chapter 4.2.3 --- Segmentation from Frame-Mosaic Comparison --- p.40 / Chapter 4.3 --- Compression of the Foreground Regions --- p.44 / Chapter 4.3.1 --- MPEG-1 Compression --- p.44 / Chapter 4.3.2 --- MPEG Coding Method: I/P/B Frames --- p.45 / Chapter 4.4 --- Video Stream Reconstruction --- p.48 / Chapter 5 --- Three Dimensional Human Face modeling --- p.52 / Chapter 5.1 --- Capturing Images for 3D Face modeling --- p.53 / Chapter 5.2 --- Shape Estimation and Model Deformation --- p.55 / Chapter 5.2.1 --- Head Shape Estimation and Model deformation --- p.55 / Chapter 5.2.2 --- Face organs shaping and positioning --- p.58 / Chapter 5.2.3 --- Reconstruction with both intrinsic and extrinsic parameters --- p.59 / Chapter 5.2.4 --- Reconstruction with only Intrinsic Parameter --- p.63 / Chapter 5.2.5 --- Essential Matrix --- p.65 / Chapter 5.2.6 --- Estimation of Essential Matrix --- p.66 / Chapter 5.2.7 --- Recovery of 3D Coordinates from Essential Matrix --- p.67 / Chapter 5.3 --- Integration of Head Shape and Face Organs --- p.70 / Chapter 5.4 --- Texture-Mapping --- p.71 / Chapter 6 --- Experimental Result & Discussion --- p.74 / Chapter 6.1 --- Panoramic Video Representation --- p.74 / Chapter 6.1.1 --- Compression Improvement from Foreground Extraction --- p.76 / Chapter 6.1.2 --- Video Compression Performance --- p.78 / Chapter 6.1.3 --- Quality of Reconstructed Video Sequence --- p.80 / Chapter 6.2 --- 3D Face model Reconstruction --- p.91 / Chapter 7 --- Conclusion and Future Direction --- p.94 / Bibliography --- p.101

by Lai Hin Lun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 54-57). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Table of Contents --- p.v / List of Figures --- p.vii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Related Works --- p.8 / Chapter 2.1 --- Source Modeling --- p.9 / Chapter 2.2 --- CBR Scheduler for VBR Delivery --- p.11 / Chapter 2.3 --- Brute Force Scheduler: --- p.15 / Chapter 2.4 --- Temporal Smoothing Scheduler: --- p.16 / Chapter Chapter 3 --- Decreasing Rate Scheduling --- p.22 / Chapter 3.1 --- MDRS with Minimum Buffer Requirement --- p.25 / Chapter 3.2 --- 2-Rate MDRS --- p.31 / Chapter Chapter 4 --- Performance Evaluation --- p.33 / Chapter 4.1 --- Buffer Requirement --- p.35 / Chapter 4.2 --- Startup Delay --- p.38 / Chapter 4.3 --- Disk Utilization --- p.39 / Chapter 4.4 --- Complexity --- p.43 / Chapter Chapter 5 --- Conclusion --- p.49 / Appendix --- p.51 / Bibliography --- p.54

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