published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

Wang Tong. / Thesis submitted in: June 2004. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 64-69). / Abstracts in English and Chinese. / ABSTRACT --- p.I / 摘要 --- p.IV / ACKNOWLEDGMENTS --- p.VI / TABLE OF CONTENTS --- p.VIII / LIST OF FIGURES --- p.XIIV / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Hough Transform --- p.2 / Chapter 1.2 --- Template Matching --- p.3 / Chapter 1.3 --- Genetic Algorithms --- p.4 / Chapter 1.4 --- Outline of the Thesis --- p.6 / Chapter CHAPTER 2 --- HOUGH TRANSFORM AND ITS COMMON VARIANTS --- p.7 / Chapter 2.1 --- Hough Transform --- p.7 / Chapter 2.1.1 --- What is Hough Transform --- p.7 / Chapter 2.1.2 --- Parameter Space --- p.7 / Chapter 2.1.3 --- Accumulator Array --- p.9 / Chapter 2.2 --- Gradient-based Hough Transform --- p.10 / Chapter 2.2.1 --- Direction of Gradient --- p.11 / Chapter 2.2.2 --- Accumulator Array --- p.14 / Chapter 2.2.3 --- Peaks in the accumulator array --- p.16 / Chapter 2.2.4 --- Performance of Gradient-based Hough Transform --- p.18 / Chapter 2.3 --- Generalized Hough Transform (GHT) --- p.19 / Chapter 2.3.1 --- What Is GHT --- p.19 / Chapter 2.3.2 --- R-table of GHT --- p.20 / Chapter 2.3.3 --- GHT Procedure --- p.21 / Chapter 2.3.4 --- Analysis --- p.24 / Chapter 2.4 --- Edge Detection --- p.25 / Chapter 2.4.1 --- Gradient-Based Method --- p.25 / Chapter 2.4.2 --- Laplacian of Gaussian --- p.29 / Chapter 2.4.3 --- Canny edge detection --- p.30 / Chapter CHAPTER 3 --- PROBABILISTIC MODELS --- p.33 / Chapter 3.1 --- Randomized Hough Transform (RHT) --- p.33 / Chapter 3.1.1 --- Basics of the RHT --- p.33 / Chapter 3.1.2 --- RHT algorithm --- p.34 / Chapter 3.1.3 --- Advantage of RHT --- p.37 / Chapter 3.2 --- Genetic Model --- p.37 / Chapter 3.2.1 --- Genetic algorithm mechanism --- p.38 / Chapter 3.2.2 --- A Genetic Algorithm for Primitive Extraction --- p.39 / Chapter CHAPTER 4 --- PROPOSED ARBITRARY SHAPE DETECTION --- p.42 / Chapter 4.1 --- Randomized Generalized Hough Transform --- p.42 / Chapter 4.1.1 --- R-table properties and the general notion of a shape --- p.42 / Chapter 4.1.2 --- Using pairs of edges --- p.44 / Chapter 4.1.3 --- Extend to Arbitrary shapes --- p.46 / Chapter 4.2 --- A Genetic algorithm with the Hausdorff distance --- p.47 / Chapter 4.2.1 --- Hausdorff distance --- p.47 / Chapter 4.2.2 --- Chromosome strings --- p.48 / Chapter 4.2.3 --- Discussion --- p.51 / Chapter CHAPTER 5 --- EXPERIMENTAL RESULTS AND COMPARISONS --- p.52 / Chapter 5.1 --- Primitive extraction --- p.53 / Chapter 5.2 --- Arbitrary Shape Detection --- p.54 / Chapter 5.3 --- Summary of the Experimental Results --- p.60 / Chapter CHAPTER 6 --- CONCLUSIONS --- p.62 / Chapter 6.1 --- Summary --- p.62 / Chapter 6.2 --- Future work --- p.63 / BIBLIOGRAPHY --- p.64

Shan, Qi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 124-132). / Abstracts in English and Chinese. / Chapter 1 --- Overview --- p.1 / Chapter 1.1 --- Image Blur Overview --- p.1 / Chapter 1.2 --- Blur Identification in a Transparency's Perspective --- p.3 / Chapter 1.3 --- From Transparencies to Natural Image Priors --- p.7 / Chapter 1.4 --- Discussion of the Linear Motion Model --- p.9 / Chapter 1.5 --- Binary Texture Restoration and High-Order MRF Optimization --- p.9 / Chapter 2 --- A Review on Previous Work --- p.13 / Chapter 2.1 --- Spatially-Invariant Blur Recovery --- p.13 / Chapter 2.2 --- Spatially-Variant Blur Recovery --- p.16 / Chapter 2.3 --- Markov Random Field Inference --- p.18 / Chapter 3 --- Motion Blur in a Transparency's Perspective --- p.20 / Chapter 3.1 --- Analysis of Object Motion Blur --- p.20 / Chapter 3.1.1 --- 1D Object Motion Blur --- p.20 / Chapter 3.1.2 --- 2D Object Motion Blur --- p.23 / Chapter 3.2 --- Modeling 2D Object Motion Blur --- p.26 / Chapter 3.3 --- Optimization Procedure --- p.27 / Chapter 3.3.1 --- Blur Kernel Estimation --- p.29 / Chapter 3.3.2 --- Latent Binary Matte Estimation --- p.30 / Chapter 3.4 --- Generalized Transparency in Motion Blur --- p.33 / Chapter 3.4.1 --- Camera Motion Blur Estimation --- p.35 / Chapter 3.4.2 --- Implementation --- p.37 / Chapter 3.5 --- Analysis and Results --- p.38 / Chapter 3.5.1 --- Evaluation of the Kernel Initialization --- p.40 / Chapter 3.5.2 --- Evaluation of Binary Alpha Initialization --- p.40 / Chapter 3.5.3 --- Robustness to Noise --- p.41 / Chapter 3.5.4 --- Natural Image Deblurring Results --- p.41 / Chapter 3.6 --- Proofs --- p.50 / Chapter 4 --- Rotational Motion Deblurring --- p.55 / Chapter 4.1 --- Motion blur descriptor --- p.55 / Chapter 4.1.1 --- Descriptor analysis --- p.56 / Chapter 4.2 --- Optimization --- p.59 / Chapter 4.2.1 --- Parameter initialization --- p.59 / Chapter 4.2.2 --- Iterative optimization --- p.62 / Chapter 4.2.3 --- Recover the color image --- p.65 / Chapter 4.3 --- Result and analysis --- p.65 / Chapter 5 --- Image Deblurring using Natural Image Priors --- p.70 / Chapter 5.1 --- Problem Definition --- p.70 / Chapter 5.2 --- Analysis of Ringing Artifacts --- p.71 / Chapter 5.3 --- Our model --- p.74 / Chapter 5.3.1 --- Definition of the probability terms --- p.75 / Chapter 5.4 --- Optimization --- p.81 / Chapter 5.4.1 --- Optimizing L --- p.83 / Chapter 5.4.2 --- Optimizing f --- p.86 / Chapter 5.4.3 --- Optimization Details and Parameters --- p.87 / Chapter 5.5 --- Experimental Results --- p.90 / Chapter 6 --- High Order MRF and its Optimization --- p.94 / Chapter 6.1 --- The Approach --- p.95 / Chapter 6.1.1 --- Polynomial Standardization --- p.95 / Chapter 6.1.2 --- Polynomial Graph Construction --- p.97 / Chapter 6.1.3 --- Polynomial Graph Partition --- p.103 / Chapter 6.1.4 --- Multi-Label Expansion --- p.105 / Chapter 6.1.5 --- Analysis --- p.106 / Chapter 6.2 --- Experimental Results --- p.108 / Chapter 6.3 --- Summary --- p.112 / Chapter 6.4 --- Proofs --- p.112 / Chapter 7 --- Conclusion --- p.117 / Chapter 7.1 --- Solving Linear Motion Blur in a Transparency's Perspective --- p.117 / Chapter 7.2 --- Rotational Motion Deblurring --- p.119 / Chapter 7.3 --- Image Deblurring using Natural Image Priors --- p.119 / Chapter 7.4 --- Contribution --- p.121 / Chapter 7.5 --- Discussion and Open Questions --- p.121 / Bibliography --- p.124

Huang, Ting. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 37-41). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Related Area --- p.2 / Chapter 1.2 --- Previous Work --- p.4 / Chapter 1.3 --- Proposed Framework --- p.7 / Chapter 1.4 --- Overview --- p.8 / Chapter 2 --- Markov Random Fields and Optimization Schemes --- p.9 / Chapter 2.1 --- MRF Model --- p.10 / Chapter 2.1.1 --- MAP Understanding --- p.11 / Chapter 2.2 --- Belief Propagation Optimization Scheme --- p.14 / Chapter 2.2.1 --- Max-Product BP on MRFs --- p.14 / Chapter 2.2.2 --- Sum-Product BP on MRFs --- p.15 / Chapter 3 --- Our Formulation --- p.17 / Chapter 3.1 --- An MRF Model --- p.18 / Chapter 3.2 --- Coarse-to-Fine Optimization by BP --- p.21 / Chapter 3.2.1 --- Coarse-Level Belief Propagation --- p.23 / Chapter 3.2.2 --- Fine-Level Belief Propagation --- p.24 / Chapter 3.2.3 --- Performance Enhancement --- p.25 / Chapter 4 --- Experiments --- p.27 / Chapter 4.1 --- Comparison --- p.27 / Chapter 4.2 --- Failure Case --- p.32 / Chapter 5 --- Conclusion --- p.35 / Bibliography --- p.37

Active binocular vision systems are powerful tools in machine vision. With a virtually unlimited field-of-view they have access to huge amounts of information, yet are able to confine their resources to specific regions of interest. Since they can dynamically interact with the environment, they are able to successfully address problems that are ill-posed to passive systems. A primary goal of an active binocular vision systems is to ascertain depth information. Since they employ two cameras and are able to sample a scene from two distinct vantage points, they are well suited for such a task. The depth recovery process is composed of two interrelated components: image registration and sampling. Image registration is the process of determining corresponding points between the stereo images. Once points in the images have been matched, 3D information can be recovered via triangulation. Image sampling determines how the image is discretized and represented. Image registration and sampling are highly interdependent. The choice of sampling scheme can profoundly impact the accuracy and complexity of the registrations process. In many situations, particular registration algorithms are simply incompatible with some sampling schemes. In this dissertation we meticulously address both registration and sampling in the context of stereopis for active binocular vision systems. Throughout the development of this work, contributions in each area are addressed with an eye toward their eventual integration into a cohesive registration procedure appropriate for active binocular vision systems. The actual synthesis is a daunting task that is beyond the scope of this single dissertation. The focus of this work is to assiduously analyze both registration and sampling, establishing a solid foundation for their future aggregation. One of the most successful approaches to image registration is phase-differencing. Phase-differencing algorithms provide a fast, powerful means for depth recovery. Unfortunately, phase-differencing techniques suffer from two significant impediments: phase nonlinearities and neglect of multispectral information. This dissertation uses the amenable properties of white noise images to analytically quantify the behavior of phase in these regions of phase nonlinearity. The improved understanding gained from this analysis enables us to create a new, more effective method for identifying these regions based on the second derivative of phase. We also suggest a novel approach that combines our method of nonlinear phase detection with strategies of both phase-differencing and local correlation. This hybrid approach retains the advantageous properties of phase-differencing while incorporating the multispectral aspects of local correlation. This task of registration is greatly simplified if the camera geometry is known and the search for corresponding points can be restricted to epipolar lines. Unfortunately, computation of epipolar lines for an active system requires calibration which can be both highly complex and inaccurate. While it is possible to register images without calibration information, such unconstrained algorithms are usually time consuming and prone to error. In this dissertation we propose compromise. Even without the instantaneous knowledge of the system geometry, we can restrict the region of correspondence by imposing limits on the possible range of configurations, and as a result, confine our search for matching points to what we refer to as epipolar spaces. For each point in one image, we define the corresponding epipolar space in the other image as the union of all associated epipolar lines over all possible system geometries. Epipolar spaces eliminate the need for calibration at the cost of an increased search region. Since the average size of a search space is directly related to the accuracy and efficiency of any registration algorithm, it is essential to mitigate the increase. The major contribution of this dissertation is the derivation of an optimal nonuniform sampling that minimizes the average area per epipolar space. / text

The object of this thesis is to present two novel applications of Spatiotemporal Image Correlation Spectroscopy (STICS) to biological systems. STICS is a technique which uses the correlations in pixel intensity fluctuations of an image time series, captured under fluorescence microscopy, to measure the speed and direction of a flowing population of fluorescently labeled molecules. The method was first applied to measure the dynamics of transport vesicles inside growing pollen tubes of lily flowers. The measured vector maps allowed to confirm the presence of actin filaments along the periphery of the tubes, as well as the presence of a reverse-fountain pattern in the apical region. In a second set of experiments, STICS was used to measure the retrograde flow of filamentous actin in migrating chick DRG neuronal growth cones. These results serve as proof of principle that STICS can be used to probe the response of the growth cone cytoskeleton to external chemical cues.

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by Shen Hua. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 67-71). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgments --- p.v / Table of Contents --- p.vii / List of Figures --- p.ix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Object Detection --- p.2 / Chapter 1.2 --- Signboard Detection --- p.3 / Chapter Chapter 2 --- System Overview --- p.5 / Chapter 2.1 --- What is the problem? --- p.5 / Chapter 2.2 --- Review of previous work --- p.6 / Chapter 2.3 --- System Outline --- p.8 / Chapter Chapter 3 --- Preprocessing --- p.10 / Chapter 3.1 --- Edge Detection --- p.11 / Chapter 3.1.1 --- Gradient-Based Method --- p.11 / Chapter 3.1.2 --- Laplacian of Gaussian --- p.14 / Chapter 3.1.3 --- Canny edge detection --- p.15 / Chapter 3.2 --- Corner Detection --- p.18 / Chapter Chapter 4 --- Finding Candidate Lines --- p.22 / Chapter 4.1 --- Hough Transform --- p.22 / Chapter 4.1.1 --- What is Hough Transform --- p.22 / Chapter 4.1.2 --- Parameter Space --- p.22 / Chapter 4.1.3 --- Accumulator Array --- p.24 / Chapter 4.2 --- Gradient-based Hough Transform --- p.25 / Chapter 4.2.1 --- Direction of Gradient --- p.26 / Chapter 4.2.2 --- Accumulator Array --- p.28 / Chapter 4.2.3 --- Peaks in the accumulator array --- p.30 / Chapter 4.2.4 --- Performance of Gradient-based Hough Transform --- p.32 / Chapter Chapter 5 --- Signboards Locating --- p.35 / Chapter 5.1 --- Line Verification --- p.35 / Chapter 5.1.1 --- Line Segmentation --- p.35 / Chapter 5.1.2 --- Density Checking --- p.37 / Chapter 5.2 --- Finding Close Circuits --- p.40 / Chapter 5.3 --- Remove Redundant Segments --- p.47 / Chapter Chapter 6 --- Post processing --- p.54 / Chapter Chapter 7 --- Experiments and Conclusion --- p.59 / Chapter 7.1 --- Experimental Results --- p.59 / Chapter 7.2 --- Conclusion --- p.66 / Bibliography --- p.67

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Li, Xiang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 161-184). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract and appendix also in Chinese.