Another objective of this work is to study the perceptual optimized video object coding. Since MPEG-4 treats a scene as a composition of video objects that are separately encoded and decoded, such a flexible video coding framework makes it possible to code different video objects with different priorities. It is necessary to analyze the priorities of video objects according to their intrinsic properties and psycho-visual characteristics such that the bit budget can be distributed properly to video objects to improve the perceptual quality of the compressed video. An object-level visual attention model is developed to automatically obtain the visual attention information of video objects. The visual attention values of video objects are calculated and incorporated in the newly developed dynamic bit allocation mechanism to improve the objective quality of the high priority objects such that the perceptual quality of the overall picture can be maximized. / As strict rate control algorithms used in video coding sacrifice the quality consistency, the rate distortion tradeoff is important to achieve a balance between the bit rate and quality. A novel separable rate distortion modeling method is proposed to analyze the rate distortion characteristics of the color video signal. This method provides higher estimation accuracy when compared to the non-separable modeling method. To achieve rate distortion tradeoff in H.264/AVC, a new control strategy is presented. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach to avoid buffer overflow and underflow. A novel rate distortion tradeoff controller is designed by considering both the quality variation and buffer fluctuation. Smooth video quality is achieved and the relevant constraints are satisfied. / Due to the unique features of the video object coding such as both texture and shape introducing distortions and video objects being of arbitrarily shapes, the rate distortion analysis and optimization strategies are different from the traditional rectangular frame-based techniques. Two new rate distortion modeling methods are proposed for the shape coding. The first one is a linear rate distortion modeling method. The computational complexity is low and the estimation is accurate. To further improve the modeling performance, a novel statistical learning based method is proposed by incorporating shape features to provide rate distortion analysis for the shape coding. Therefore, a joint texture-shape rate distortion modeling approach is derived by integrating the texture and shape rate distortion models. The new joint texture-shape distortion models provide the basis for optimal bit allocation for the video object coding to minimize the coding distortion with the bit rate constraint and stabilize the buffer fullness. The major contribution of this optimal bit allocation scheme is to provide a unified solution for the following two problems: how to allocate bits between the texture and shape and how to distribute the hit budget for multiple video objects, simultaneously. / This thesis addresses rate distortion analysis, optimization, and control problems in video coding. These rate distortion issues not only provide the theoretical background but also are concerned with the practical design for video coding systems. The main objective of this thesis is to consider the problems associated with analyzing the rate distortion characteristics of the video source and providing optimal solutions or tradeoffs for the rate and distortion in video coding systems. More specifically this thesis focuses on both the object-based video coding system, MPEG-4, and the rectangular frame-based video coding system, H.264/AVC. / Chen, Zhenzhong. / "July 2007." / Adviser: King Ngi Ngan. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1194. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 225-247). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

In this thesis, we primarily focus on one common type of symmetry, the translational symmetry. We first review the current state-of-the-art methods for translational symmetry detection, and discuss their benefits and drawbacks. Towards an efficient, automatic and widely applicable translational symmetry detector, we develop a novel method for automatically detecting translational symmetry patterns, and extracting the corresponding lattices from images without pre-segmentation or reconstructing the underlying 3D geometry. In particular, we employ a region-based feature and fully utilize its regional properties (shape, orientation and well-defined boundary) to propose the repeated candidates. Compared with traditional treatments, which usually rely on point-based features and group them to propose repeated candidates, our treatment is more efficient and stable to perspective projection, distortion or noise. By clustering the candidate regions and indexing the major clusters using a GPU KD-tree, the parallel lattice formation processes turn out to be very efficient and achieve a real-time rate. By using a set of spatially varying vectors with a loose neighboring constraint to represent the underlying lattice, we successfully detect most of translational symmetry patterns over arbitrary surfaces, which can be planar or curve, without or with perspective projection, and even when suffered from global and local deformations. Moreover, the parallel searching and saving scheme enables us to simultaneously detect multiple disjoint symmetry patterns from an input images. / Symmetry has been an important concept in the nature, science and art. There is an abundant of biological, chemical, and artificial structures captured in many real-world images, exhibiting various forms of symmetry. The symmetry patterns and the repetitive elements reinforce the visual importance and usually make an image more attractive. Although our humans have an excellent innate ability in recognizing symmetry and perceiving its beauty, efficient and automatic symmetry detection from images remains a unsolved challenging problem in computer vision and graphics. Without understanding the high-level semantics of symmetry, editing such images while preserving the repetitions and their relations turns out to be difficult to perform, such as image resizing, image inpainting and image replacement. / The significant improvements of our method in both efficiency and accuracy make it a useful tool from which many applications can benefit. One of them is image resizing. We demonstrate that image resizing can be achieved more effectively if we have a better understanding of the image semantics. By analyzing the translational symmetry patterns, and detecting the underlying lattices in an image, we can summarize, instead of only distorting or cropping, the image content. This opens a new space for image resizing that allows us to manipulate, not only image pixels, but also the semantic cells in the lattice. As a general image contains both symmetry & non-symmetry regions and their natures are different, we propose to resize symmetry regions by summarization and non-symmetry region by optimized warping. In addition, by smoothing the intensity of cells across the lattice, we can further maintain the seamlessness of illumination during the summarization. As the difference in resizing strategy between symmetry regions and non-symmetry region leads to discontinuity at their shared boundary, we propose a framework to minimize the artifact. Experimental results show that, with the high-level knowledge of symmetry, our method outperforms the state-of-the-art resizing techniques. / Wu, Huisi. / Advisers: Tien-Tsin Wong; Pheng-Ann Heng. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 92-100). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

by Wai Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 148-150). / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Image Selection/Segmentation --- p.3 / Chapter 1.2. --- Background Image Generation --- p.5 / Chapter 1.3. --- Thesis Organisation --- p.6 / Chapter 2. --- Fundamentals of Digital Image Segmentation --- p.7 / Chapter 2.1. --- Edge-based Segmentation methods --- p.7 / Chapter 2.1.1. --- Edge detection --- p.7 / Chapter 2.1.1.1. --- Gradient operators --- p.9 / Chapter 2.1.1.2. --- Compass operators --- p.10 / Chapter 2.1.1.3. --- Laplace operators and zero crossings --- p.10 / Chapter 2.1.1.4. --- Stochastic gradients --- p.11 / Chapter 2.1.1.5. --- Optimal edge detectors --- p.12 / Chapter 2.1.2. --- Boundary extraction --- p.13 / Chapter 2.1.2.1. --- Contour following --- p.13 / Chapter 2.1.2.2. --- Heuristic graph searching --- p.14 / Chapter 2.1.2.3. --- Dynamic programming --- p.14 / Chapter 2.2. --- Region-based Segmentation Methods --- p.15 / Chapter 2.2.1. --- Thresholding --- p.15 / Chapter 2.2.2. --- Region growing --- p.16 / Chapter 2.2.3. --- Region splitting and merging --- p.17 / Chapter 2.2.4. --- Texture segmentation --- p.19 / Chapter 2.2.4.1. --- Spectral approaches --- p.19 / Chapter 2.2.4.2. --- Statistical methods --- p.21 / Chapter 2.3. --- Works on Colour Image Segmentation --- p.23 / Chapter 3. --- Current Selection Tools for Image Retouching --- p.24 / Chapter 3.1. --- Selection by Region Growing --- p.24 / Chapter 3.2. --- Selection by Edge Finding --- p.26 / Chapter 3.3. --- Some Conclusions --- p.27 / Chapter 4. --- A New Segmentation Tool for Image Retouching --- p.28 / Chapter 4.1. --- Requirement and Development Strategy of the Selection Tool --- p.28 / Chapter 4.2. --- Basic Assumptions --- p.29 / Chapter 4.3. --- Algorithm of the Image Selector --- p.30 / Chapter 4.3.1. --- Boundary representation --- p.30 / Chapter 4.3.2. --- Colour edge detection --- p.31 / Chapter 4.3.2.1. --- Colour gradient --- p.31 / Chapter 4.3.2.2. --- Edge detector --- p.32 / Chapter 4.4. --- Boundary Searching --- p.34 / Chapter 4.4.1. --- The searching algorithm - A* --- p.35 / Chapter 4.4.2. --- The cost function and heuristic for A* algorithm --- p.37 / Chapter 4.4.2.1. --- Pixel cost formulation --- p.38 / Chapter A. --- The reference dependent function y --- p.39 / Chapter 1. --- The three similarity functions --- p.42 / Chapter 2. --- The similarity thresholds --- p.44 / Chapter 3. --- The n-reference dependent function --- p.45 / Chapter B. --- The reference independent function f --- p.46 / Chapter C. --- The actual pixel cost function W --- p.46 / Chapter 4.4.2.2. --- The heuristic function h --- p.47 / Chapter 4.5. --- Implementation --- p.48 / Chapter 4.5.1. --- Work-flow of the image selection tool --- p.48 / Chapter 4.5.2. --- Implementation of the user-input stage --- p.51 / Chapter 4.5.3. --- Implementation of the boundary searching phase --- p.54 / Chapter 4.5.3.1. --- OPEN and CLOSE lists --- p.56 / Chapter 4.5.3.2. --- Tracing back searching path --- p.57 / Chapter 4.5.3.3. --- Edge map --- p.58 / Chapter 4.5.3.4. --- Cost calculation --- p.59 / Chapter 4.5.4. --- Implementation of the boundary connection phase --- p.61 / Chapter 4.6. --- Experiments and Results --- p.63 / Chapter 4.6.1. --- Features exploration --- p.63 / Chapter 4.6.1.1. --- Cost function of the boundary tracer --- p.63 / Chapter 4.6.1.2. --- Reference sensitivity --- p.73 / Chapter 4.6.1.3. --- Boundary connection --- p.75 / Chapter 4.6.2. --- Comparison with current image selection tools --- p.76 / Chapter 4.7. --- Discussion --- p.87 / Chapter 4.8. --- Further Improvement --- p.91 / Chapter 4.8.1. --- A* algorithm --- p.91 / Chapter 4.8.2. --- Colour space --- p.92 / Chapter 4.8.3. --- Improvement in processing speed and quality --- p.93 / Chapter 5. --- Background Image Generation by Image Interpolation --- p.95 / Chapter 5.1. --- Current Filling Tools for Background Image Generation --- p.96 / Chapter 5.1.1. --- The Stamp tool --- p.96 / Chapter 5.1.2. --- The Gradient Fill tool --- p.96 / Chapter 5.2. --- Possible Approaches --- p.98 / Chapter 5.2.1. --- Surface Approximation --- p.98 / Chapter 5.2.2. --- Region Growing and Filling --- p.99 / Chapter 5.3. --- A New Image Interpolation Tool --- p.101 / Chapter 5.3.1. --- Problem analysis and requirement specifications --- p.101 / Chapter 5.3.2. --- The interpolation strategy --- p.103 / Chapter 5.3.3. --- Process overview of the image interpolation --- p.107 / Chapter 5.3.4. --- Data representation --- p.108 / Chapter 5.3.5. --- Implementation --- p.110 / Chapter 5.3.5.1. --- User-input - the first stage --- p.110 / Chapter 5.3.5.2. --- Interpolation preparation - the second stage --- p.112 / Chapter A. --- Boundary list rearrangement --- p.113 / Chapter B. --- Boundary parameterisation --- p.114 / Chapter C. --- Area marking --- p.114 / Chapter D. --- Colour pattern preparation --- p.115 / Chapter 5.3.5.3. --- Interpolation - the third stage --- p.118 / Chapter A. --- Filling curve generation --- p.119 / Chapter 1. --- Linear filling curve generation --- p.119 / Chapter 2. --- Non-linear filling curve generation --- p.120 / Chapter B. --- Colour pattern interpolation --- p.125 / Chapter 5.3.5.4. --- Post-filling - the fourth stage --- p.127 / Chapter 5.3.5.5. --- A working example --- p.128 / Chapter 5.3.6. --- Results --- p.130 / Chapter 5.3.7. --- Discussion --- p.140 / Chapter 5.3.8. --- Limitations and future improvement --- p.141 / Chapter 6. --- Conclusions --- p.144 / Appendix --- p.147 / References --- p.148

by Chan Ming-hong. / Bibliography: leaves 67-68 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1986

by Wong Hin Lau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 118-126).

Woo Hok Luen. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 42-43). / Abstract --- p.2 / Chapter Ch. 1 --- Introduction --- p.3 / Chapter Ch. 2 --- Review --- p.4 / Chapter 2.1 --- Definitions and notions --- p.4 / Chapter 2.2 --- Features of a skeleton --- p.5 / Chapter 2.3 --- Parallel and sequential algorithms --- p.6 / Chapter 2.4 --- Distance transformations --- p.7 / Chapter 2.5 --- Relaxation labelling process --- p.8 / Chapter Ch. 3 --- The proposed algorithm --- p.9 / Chapter 3.1 --- Definitions and notions --- p.9 / Chapter 3.2 --- The thinning problem --- p.13 / Chapter 3.3 --- Assigning initial probabilities --- p.14 / Chapter 3.4 --- Iteration schemes --- p.15 / Chapter 3.5 --- Net increment of the skeletal probabilities --- p.16 / Chapter 3.6 --- Net increment of the nonskeletal probabilities --- p.16 / Chapter 3.7 --- Terminating condition --- p.19 / Chapter Ch. 4 --- Experimental results --- p.21 / Chapter 4.1 --- Parameters --- p.21 / Chapter 4.2 --- Results --- p.23 / Chapter Ch. 5 --- Discussion --- p.29 / Chapter 5.1 --- 4-neighbour and 8-neighbour DT --- p.30 / Chapter 5.2 --- 8-connectivity number and safe-point tests --- p.32 / Chapter 5.3 --- Mutual exclusion problem --- p.33 / Chapter 5.4 --- Skeleton not of unit width --- p.34 / Chapter 5.5 --- Development --- p.35 / Chapter Ch. 6 --- Conclusion --- p.35 / Appendix --- p.36 / Reference --- p.42

by Sai-bun Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 127-131). / Abstract --- p.v / Acknowledgment --- p.vii / Chapter 1 --- Introduction --- p.1-8 / Chapter 1.1 --- Overview --- p.2 / Chapter 1.2 --- Survey --- p.4 / Chapter 1.3 --- Objectives --- p.6 / Chapter 1.4 --- Thesis Organization --- p.7 / Chapter 2 --- Range Sensing --- p.9-30 / Chapter 2.1 --- Alternative Approaches to Range Sensing --- p.9 / Chapter 2.1.1 --- Size Constancy --- p.9 / Chapter 2.1.2 --- Defocusing --- p.11 / Chapter 2.1.3 --- Deconvolution --- p.14 / Chapter 2.1.4 --- Binolcular Vision --- p.18 / Chapter 2.1.5 --- Active Triangulation --- p.20 / Chapter 2.1.6 --- Time-of-Flight --- p.22 / Chapter 2.2 --- Transmitter and Detector in Active Sensing --- p.26 / Chapter 2.2.1 --- Acoustics --- p.26 / Chapter 2.2.2 --- Optics --- p.28 / Chapter 2.2.3 --- Microwave --- p.29 / Chapter 2.3 --- Conclusion --- p.29 / Chapter 3 --- Scanning Mirror --- p.31-47 / Chapter 3.1 --- Scanning Mechanisms --- p.31 / Chapter 3.2 --- Advantages of Scanning Mirror --- p.32 / Chapter 3.3 --- Feedback of Scanning Mirror --- p.33 / Chapter 3.4 --- Scanning Mirror Controller --- p.35 / Chapter 3.5 --- Point-to-Point Scanning --- p.39 / Chapter 3.6 --- Line Scanning --- p.39 / Chapter 3.7 --- Specifications and Measurements --- p.41 / Chapter 4 --- The Rangefinder with Reflectance Sensing --- p.48-58 / Chapter 4.1 --- Ambient Noises --- p.49 / Chapter 4.2 --- Occlusion/Shadow --- p.49 / Chapter 4.3 --- Accuracy and Precision --- p.50 / Chapter 4.4 --- Optics --- p.53 / Chapter 4.5 --- Range/Reflectance Crosstalk --- p.56 / Chapter 4.6 --- Summary --- p.58 / Chapter 5 --- Computer Generation of Range Map --- p.59-75 / Chapter 5.1 --- Homogenous Transformation --- p.61 / Chapter 5.2 --- From Global to Viewer Coordinate --- p.63 / Chapter 5.3 --- Z-buffering --- p.55 / Chapter 5.4 --- Generation of Range Map --- p.66 / Chapter 5.5 --- Experimental Results --- p.68 / Chapter 6 --- Characterization of Range Map --- p.76-90 / Chapter 6.1 --- Mean and Gaussian Curvature --- p.76 / Chapter 6.2 --- Methods of Curvature Generation --- p.78 / Chapter 6.2.1 --- Convolution --- p.78 / Chapter 6.2.2 --- Local Surface Patching --- p.81 / Chapter 6.3 --- Feature Extraction --- p.84 / Chapter 6.4 --- Conclusion --- p.85 / Chapter 7 --- Merging Multiple Characteristic Views --- p.91-119 / Chapter 7.1 --- Rigid Body Model --- p.91 / Chapter 7.2 --- Sub-rigid Body Model --- p.94 / Chapter 7.3 --- Probabilistic Relaxation Matching --- p.95 / Chapter 7.4 --- Merging the Sub-rigid Body Model --- p.99 / Chapter 7.5 --- Illustration --- p.101 / Chapter 7.6 --- Merging Multiple Characteristic Views --- p.104 / Chapter 7.7 --- Mislocation of Feature Extraction --- p.105 / Chapter 7.7.1 --- The Transform Matrix for Perfect Matching --- p.106 / Chapter 7.7.2 --- Introducing The Errors in Feature Set --- p.108 / Chapter 7.8 --- Summary --- p.113 / Chapter 8 --- Conclusion --- p.120-126 / References --- p.127-131 / Appendix A - Projection of Object --- p.A1-A2 / Appendix B - Performance Analysis on Rangefinder System --- p.B1-B16 / Appendix C - Matching of Two Characteristic views --- p.C1-C3

Chow Kai Tik = 支援矢量回歸法之影像表示式及其影像處理與分析 / 周啓迪. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 380-383). / Text in English; abstracts in English and Chinese. / Chow Kai Tik = Zhi yuan shi liang hui gui fa zhi ying xiang biao shi shi ji qi ying xiang chu li yu fen xi / Zhou Qidi. / Abstract in English / Abstract in Chinese / Acknowledgement / Content / List of figures / Chapter Chapter 1 --- Introduction --- p.1-11 / Chapter 1.1 --- Introduction --- p.2 / Chapter 1.2 --- Road Map --- p.9 / Chapter Chapter 2 --- Review of Support Vector Machine --- p.12-124 / Chapter 2.1 --- Structural Risk Minimization (SRM) --- p.13 / Chapter 2.1.1 --- Introduction / Chapter 2.1.2 --- Structural Risk Minimization / Chapter 2.2 --- Review of Support Vector Machine --- p.21 / Chapter 2.2.1 --- Review of Support Vector Classification / Chapter 2.2.2 --- Review of Support Vector Regression / Chapter 2.2.3 --- Review of Support Vector Clustering / Chapter 2.2.4 --- Summary of Support Vector Machines / Chapter 2.3 --- Implementation of Support Vector Machines --- p.60 / Chapter 2.3.1 --- Kernel Adatron for Support Vector Classification (KA-SVC) / Chapter 2.3.2 --- Kernel Adatron for Support Vector Regression (KA-SVR) / Chapter 2.3.3 --- Sequential Minimal Optimization for Support Vector Classification (SMO-SVC) / Chapter 2.3.4 --- Sequential Minimal Optimization for Support Vector Regression (SMO-SVR) / Chapter 2.3.5 --- Lagrangian Support Vector Classification (LSVC) / Chapter 2.3.6 --- Lagrangian Support Vector Regression (LSVR) / Chapter 2.4 --- Applications of Support Vector Machines --- p.117 / Chapter 2.4.1 --- Applications of Support Vector Classification / Chapter 2.4.2 --- Applications of Support Vector Regression / Chapter Chapter 3 --- Image Representation by Support Vector Regression --- p.125-183 / Chapter 3.1 --- Introduction of SVR Representation --- p.116 / Chapter 3.1.1 --- Image Representation by SVR / Chapter 3.1.2 --- Implicit Smoothing of SVR representation / Chapter 3.1.3 --- "Different Insensitivity, C value, Kernel and Kernel Parameters" / Chapter 3.2 --- Variation on Encoding Method [Training Process] --- p.154 / Chapter 3.2.1 --- Training SVR with Missing Data / Chapter 3.2.2 --- Training SVR with Image Blocks / Chapter 3.2.3 --- Training SVR with Other Variations / Chapter 3.3 --- Variation on Decoding Method [Testing pr Reconstruction Process] --- p.171 / Chapter 3.3.1 --- Reconstruction with Different Portion of Support Vectors / Chapter 3.3.2 --- Reconstruction with Different Support Vector Locations and Lagrange Multiplier Values / Chapter 3.3.3 --- Reconstruction with Different Kernels / Chapter 3.4 --- Feature Extraction --- p.177 / Chapter 3.4.1 --- Features on Simple Shape / Chapter 3.4.2 --- Invariant of Support Vector Features / Chapter Chapter 4 --- Mathematical and Physical Properties of SYR Representation --- p.184-243 / Chapter 4.1 --- Introduction of RBF Kernel --- p.185 / Chapter 4.2 --- Mathematical Properties: Integral Properties --- p.187 / Chapter 4.2.1 --- Integration of an SVR Image / Chapter 4.2.2 --- Fourier Transform of SVR Image (Hankel Transform of Kernel) / Chapter 4.2.3 --- Cross Correlation between SVR Images / Chapter 4.2.4 --- Convolution of SVR Images / Chapter 4.3 --- Mathematical Properties: Differential Properties --- p.219 / Chapter 4.3.1 --- Review of Differential Geometry / Chapter 4.3.2 --- Gradient of SVR Image / Chapter 4.3.3 --- Laplacian of SVR Image / Chapter 4.4 --- Physical Properties --- p.228 / Chapter 4.4.1 --- 7Transformation between Reconstructed Image and Lagrange Multipliers / Chapter 4.4.2 --- Relation between Original Image and SVR Approximation / Chapter 4.5 --- Appendix --- p.234 / Chapter 4.5.1 --- Hankel Transform for Common Functions / Chapter 4.5.2 --- Hankel Transform for RBF / Chapter 4.5.3 --- Integration of Gaussian / Chapter 4.5.4 --- Chain Rules for Differential Geometry / Chapter 4.5.5 --- Derivation of Gradient of RBF / Chapter 4.5.6 --- Derivation of Laplacian of RBF / Chapter Chapter 5 --- Image Processing in SVR Representation --- p.244-293 / Chapter 5.1 --- Introduction --- p.245 / Chapter 5.2 --- Geometric Transformation --- p.241 / Chapter 5.2.1 --- "Brightness, Contrast and Image Addition" / Chapter 5.2.2 --- Interpolation or Resampling / Chapter 5.2.3 --- Translation and Rotation / Chapter 5.2.4 --- Affine Transformation / Chapter 5.2.5 --- Transformation with Given Optical Flow / Chapter 5.2.6 --- A Brief Summary / Chapter 5.3 --- SVR Image Filtering --- p.261 / Chapter 5.3.1 --- Discrete Filtering in SVR Representation / Chapter 5.3.2 --- Continuous Filtering in SVR Representation / Chapter Chapter 6 --- Image Analysis in SVR Representation --- p.294-370 / Chapter 6.1 --- Contour Extraction --- p.295 / Chapter 6.1.1 --- Contour Tracing by Equi-potential Line [using Gradient] / Chapter 6.1.2 --- Contour Smoothing and Contour Feature Extraction / Chapter 6.2 --- Registration --- p.304 / Chapter 6.2.1 --- Registration using Cross Correlation / Chapter 6.2.2 --- Registration using Phase Correlation [Phase Shift in Fourier Transform] / Chapter 6.2.3 --- Analysis of the Two Methods for Registrationin SVR Domain / Chapter 6.3 --- Segmentation --- p.347 / Chapter 6.3.1 --- Segmentation by Contour Tracing / Chapter 6.3.2 --- Segmentation by Thresholding on Smoothed or Sharpened SVR Image / Chapter 6.3.3 --- Segmentation by Thresholding on SVR Approximation / Chapter 6.4 --- Appendix --- p.368 / Chapter Chapter 7 --- Conclusion --- p.371-379 / Chapter 7.1 --- Conclusion and contribution --- p.372 / Chapter 7.2 --- Future work --- p.378 / Reference --- p.380-383

Lau Hang-yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 121-123). / Abstracts in English and Chinese. / Abstract in English --- p.i / Abstract in Chinese --- p.iii / List of Figures --- p.ix / List of Tables --- p.xiii / Chapter Chapter 1: --- Introduction --- p.1 / Chapter 1.1 --- Objective --- p.1 / Chapter 1.2 --- Definition ofixel-based Algorithm --- p.2 / Chapter 1.3 --- Definition of Region-based Algorithm --- p.3 / Chapter 1.4 --- Definition of Graph-based Algorithm --- p.5 / Chapter 1.5 --- Colour Spaces --- p.7 / Chapter 1.5.1 --- Basics of Colour Vision --- p.7 / Chapter 1.5.2 --- RGB Model --- p.7 / Chapter 1.5.3 --- HSV Model --- p.8 / Chapter 1.6 --- Organization of Thesis --- p.9 / Chapter Chapter 2: --- 2-Mean Clustering --- p.10 / Chapter 2.1 --- Algorithm --- p.11 / Chapter 2.2 --- Experiment Results --- p.14 / Chapter 2.2.1 --- Intensity Images --- p.14 / Chapter 2.2.2 --- Colour Images (Using RGB) --- p.15 / Chapter 2.2.3 --- Colour Image (Using Hue) --- p.19 / Chapter 2.3 --- Discussions --- p.21 / Chapter 2.3.1 --- Advantages --- p.23 / Chapter 2.3.2 --- Disadvantages --- p.24 / Chapter Chapter 3: --- Region Growing with Region Adjacency Graph --- p.25 / Chapter 3.1 --- Algorithm --- p.26 / Chapter 3.1.1 --- Region Growingrocess --- p.26 / Chapter 3.1.2 --- Region Adjacency Graph Growingrocess --- p.27 / Chapter 3.2 --- Experiment Results --- p.30 / Chapter 3.2.1 --- Intensity Images --- p.30 / Chapter 3.2.2 --- Colour Images (Using RGB) --- p.33 / Chapter 3.2.3 --- Colour Image (Using Hue) --- p.37 / Chapter 3.3 --- Discussions --- p.39 / Chapter 3.3.1 --- Advantages --- p.42 / Chapter 3.3.2 --- Disadvantages --- p.43 / Chapter Chapter 4: --- Normalized Cuts --- p.45 / Chapter 4.1 --- Formulation of the Generalized Eigenvaluesystem --- p.47 / Chapter 4.2 --- Algorithm --- p.52 / Chapter 4.3 --- Modification of NC --- p.54 / Chapter 4.4 --- Experiment Results --- p.55 / Chapter 4.4.1 --- Feature Images --- p.56 / Chapter 4.4.2 --- Intensity Images --- p.64 / Chapter 4.4.3 --- Colour Images --- p.66 / Chapter 4.5 --- Discussions --- p.70 / Chapter 4.5.1 --- Advantages --- p.70 / Chapter 4.5.2 --- Disadvantages --- p.71 / Chapter Chapter 5: --- ixel-Region-Graph Method --- p.72 / Chapter 5.1 --- Algorithm --- p.74 / Chapter 5.1.1 --- Step --- p.75 / Chapter 5.1.2 --- Step R --- p.76 / Chapter 5.1.3 --- Step G --- p.77 / Chapter 5.2 --- CompareRG Method with Other Methods --- p.80 / Chapter 5.3 --- Experiment Results --- p.82 / Chapter 5.3.1 --- Feature Image --- p.82 / Chapter 5.3.2 --- Real Images - Set 1 --- p.84 / Chapter 5.3.3 --- Real Images - Set 2 --- p.87 / Chapter 5.3.4 --- Real Images - Set 3 --- p.94 / Chapter 5.3.5 --- Real Images - Set 4 --- p.96 / Chapter 5.4 --- Discussion --- p.97 / Chapter 5.4.1 --- Advantages --- p.99 / Chapter 5.4.2 --- Disadvantages --- p.99 / Chapter 5.5 --- Image Graph --- p.100 / Chapter Chapter 6: --- Conclusion --- p.103 / Appendix I --- p.107 / Appendix II --- p.120 / References --- p.121

A method has been developed using an image analysis system that simulates human print quality perception. Previous work in the area of print quality assessment has only produced methods that measure individual print quality variables, or assess small parts of an image. The image analysis system developed in this investigation is different from the previous work because it analyses the combined effects of different variables using neural network technology. In addition, measurements from an entire image can be obtained and the system can assess images irrespective of their shape. The image analysis system hardware consists of a monochrome CCD camera, a Matrox image acquisition board and a 200 MHz Pentium computer. A data pre-processing program was developed using Visual Basic version 5 to process the image data from the camera. The processed data was fed into a neural network so that empirical models of print quality could be formulated. The neural network code originated from the Matlab neural network toolbox. Backpropagation and radial basis neural network functions were used in the investigation. The hardware and software of the image analysis system were tested for non-impact printing techniques. Images of a square, a circle and text characters with dimensions of 1 cm or less were used as test images for the image analysis system. It was established that it was possible to identify the different printing processes that produced the simple shapes and text characters using the image analysis system. This was achieved by training the neural network using pre-processed image data. This produced multi-dimensional mathematical models that were used to classify the different printing processes. The classification of the different printing processes involved the objective measurement of print quality variables. Different printing processes can produce print that differs in print quality when assessed by observers. Therefore the successful classification of the printing processes demonstrated that the image analysis system could, in some cases, simulate human print quality perception. To consolidate on the preceding printing process identification result, a simulation of print quality perception was made. A neural network was trained using observer assessments of a simple pictorial image of a face. These face images were produced using a variety of different non-impact printing techniques. The neural network model was used to predict the outcomes of a further set of assessments of face images by the same observer. The accuracy of the predictions was 23 out of 24 for both the backpropagation and radial basis function neural network functions used in the test. The investigation also produced two possible practical applications for the system. Firstly, it was shown that the system has the potential to be used as a machine that can objectively assess the print quality from photocopiers. Secondly, it was demonstrated that the system might be used for forensic work, since it can identify different printing processes.