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

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

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

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

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

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