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Image splicing localization via semi-global network and fully connected conditional random fieldsCun, Xiao Dong January 2018 (has links)
University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science
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A PC/AT-based ICT image archiving system.January 1991 (has links)
by Ringo Wai-kit Lam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1991. / Includes bibliographical references. / ACKNOWLEDGEMENTS / ABSTRACT / LIST OF FIGURES --- p.i / LIST OF TABLES --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1-1 / Chapter 1.1 --- Introduction --- p.1-1 / Chapter 1.2 --- Transform Coding Theory --- p.1-2 / Chapter 1.2.1 --- Image Transform Coder and Decoder --- p.1-2 / Chapter 1.2.2 --- Transformation --- p.1-4 / Chapter 1.2.3 --- Bit Allocation --- p.1-5 / Chapter 1.2.4 --- Quantization --- p.1-7 / Chapter 1.2.5 --- Entropy Coding --- p.1-8 / Chapter 1.2.6 --- Error of Transform Coding --- p.1-9 / Chapter 1.3 --- Organization of The Thesis --- p.1-10 / Chapter CHAPTER 2 --- 2D INTEGER COSINE TRANSFORM CHIP SET --- p.2-1 / Chapter 2.1 --- Introduction --- p.2-1 / Chapter 2.2 --- The Integer Cosine Transform (ICT) --- p.2-2 / Chapter 2.3 --- LSI Implementation --- p.2-4 / Chapter 2.3.1 --- ICT Chip --- p.2-4 / Chapter 2.3.2 --- Data Sequencer --- p.2-7 / Chapter 2.4 --- Design Considerations --- p.2-8 / Chapter 2.4.1 --- ICT chip --- p.2-9 / Chapter 2.4.1.1 --- Specifications --- p.2-9 / Chapter 2.4.1.2 --- I/O Bit Length Consideration --- p.2-10 / Chapter 2.4.1.3 --- Selection of The Transform Matrix --- p.2-12 / Chapter 2.4.2 --- Data Sequencer --- p.2-16 / Chapter 2.4.2.1 --- Normal Operation --- p.2-16 / Chapter 2.4.2.2 --- Low-pass Filtering Operation --- p.2-16 / Chapter 2.4.2.3 --- Subsampling Operation --- p.2-17 / Chapter 2.5 --- Architecture --- p.2-18 / Chapter 2.5.1 --- ICT chip --- p.2-18 / Chapter 2.5.1.1 --- Input Stage --- p.2-18 / Chapter 2.5.1.2 --- Control Block --- p.2-19 / Chapter 2.5.1.3 --- Multiplier --- p.2-19 / Chapter 2.5.1.4 --- Accumulator --- p.2-20 / Chapter 2.5.1.5 --- Output Stage --- p.2-21 / Chapter 2.5.2 --- Data Sequencer --- p.2-21 / Chapter 2.5.2.1 --- Input Stage --- p.2-22 / Chapter 2.5.2.2 --- Control Logic --- p.2-22 / Chapter 2.5.2.3 --- Internal Storage --- p.2-23 / Chapter 2.5.2.4 --- Output Stage --- p.2-24 / Chapter 2.6 --- 2D Integer Cosine Transform System --- p.2-24 / Chapter 2.6.1 --- Hardware Architecture --- p.2-24 / Chapter 2.6.2 --- Timing --- p.2-26 / Chapter 2.7 --- Conclusion --- p.2-27 / Chapter CHAPTER 3 --- A PC/AT-BASED IMAGE ARCHIVING SYSTEM --- p.3-1 / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- Design Consideration --- p.3-1 / Chapter 3.2.1 --- Specifications --- p.3-2 / Chapter 3.2.1.1 --- Operations Supported --- p.3-2 / Chapter 3.2.1.2 --- Image Formats --- p.3-3 / Chapter 3.2.1.3 --- Software --- p.3-6 / Chapter 3.2.2 --- Storage Format of the Coded Image --- p.3-6 / Chapter 3.3 --- Hardware Architecture --- p.3-8 / Chapter 3.3.1 --- Input Stage --- p.3-11 / Chapter 3.3.2 --- Inverse Transform Address Generator --- p.3-11 / Chapter 3.3.3 --- Input Memory --- p.3-13 / Chapter 3.3.3.1 --- Address Map --- p.3-14 / Chapter 3.3.3.2 --- Bit Map --- p.3-14 / Chapter 3.3.3.3 --- Class Map --- p.3-15 / Chapter 3.3.4 --- ICT Processor --- p.3-15 / Chapter 3.3.5 --- Output Memory --- p.3-16 / Chapter 3.3.6 --- Address Generator --- p.3-16 / Chapter 3.3.6.1 --- Address Generator 1 (AG1) --- p.3-17 / Chapter 3.3.6.2 --- Address Generator 2 (AG2) --- p.3-21 / Chapter 3.3.6.3 --- Address Generator 3 (AG3) --- p.3-22 / Chapter 3.3.7 --- Control Register --- p.3-22 / Chapter 3.3.8 --- Interface Consideration --- p.3-23 / Chapter 3.3.9 --- Frame Buffer --- p.3-23 / Chapter 3.4 --- Software Structure --- p.3-23 / Chapter 3.4.1 --- Main Menu --- p.3-24 / Chapter 3.4.2 --- Forward Transform --- p.3-25 / Chapter 3.4.3 --- Inverse Transform --- p.3-25 / Chapter 3.4.3.1 --- Normal --- p.3-26 / Chapter 3.4.3.2 --- Subsampling --- p.3-26 / Chapter 3.4.3.3 --- Filtering --- p.3-26 / Chapter 3.4.3.4 --- Album --- p.3-27 / Chapter 3.4.3.5 --- Display and System --- p.3-28 / Chapter 3.5 --- Conclusion --- p.3-29 / Chapter CHAPTER 4 --- SYSTEM PERFORMANCE EVALUATION --- p.4-1 / Chapter 4.1 --- Introduction --- p.4-1 / Chapter 4.2 --- Result of Image Display --- p.4-1 / Chapter 4.3 --- Computation Time Requirement --- p.4-12 / Chapter 4.4 --- Comparison to Other Transform Chips and Image Transform Systems --- p.4-16 / Chapter 4.5 --- Conclusion --- p.4-20 / Chapter CHAPTER 5 --- CONCLUSION --- p.5-1 / Chapter 5.1 --- Further Development --- p.5-1 / Chapter 5.1.1 --- Employment of JPEG Scheme --- p.5-1 / Chapter 5.1.2 --- ICT Chip Set --- p.5-5 / Chapter 5.2 --- Summary of the Image Archiving System --- p.5-6 / Chapter CHAPTER 6 --- REFERENCES --- p.6-1 / Chapter CHAPTER 7 --- APPENDIX --- p.7-1
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A hypercolumn based stereo vision model.January 1993 (has links)
by Lam Shu Sun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [91]-94). / Chapter Chapter1 --- Introduction: Binocular Depth Visual Perception of Human --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- The visual pathway --- p.2 / Chapter 1.3 --- The retina --- p.3 / Chapter 1.4 --- The ganglion cells --- p.5 / Chapter 1.5 --- The lateral geniculate nucleus --- p.7 / Chapter 1.6 --- The visual cortex --- p.8 / Chapter 1.6.1 --- The cortical cells --- p.8 / Chapter 1.6.2 --- The organization of the visual cortex --- p.9 / Chapter 1.7 --- Stereopsis --- p.11 / Chapter 1.7.1 --- Corresponding retinal points --- p.12 / Chapter 1.7.2 --- Binocular fusion --- p.14 / Chapter 1.7.3 --- The binocular depth cells --- p.14 / Chapter 1.8 --- Conclusion of chapter 1 --- p.15 / Chapter Chapter2 --- Computational Stereo Vision --- p.15 / Chapter 2.1 --- Stereo image geometry --- p.16 / Chapter 2.1.1 --- The crossed-looking geometry --- p.17 / Chapter 2.1.2 --- The parallel optical axes geometry --- p.19 / Chapter 2.2 --- The false targets problem --- p.20 / Chapter 2.3 --- Feature selection --- p.21 / Chapter 2.3.1 --- Zero-crossing method --- p.21 / Chapter 2.3.2 --- A network model for ganglion cell --- p.24 / Chapter 2.4 --- The constraints of matching --- p.28 / Chapter 2.5 --- Correspondence techniques --- p.29 / Chapter 2.6 --- Conclusion of chapter 2 --- p.29 / Chapter Chapter3 --- A Hypercolumn Based Stereo Vision Model --- p.30 / Chapter 3.1 --- A visual model for stereo vision --- p.30 / Chapter 3.2 --- The model of PSVM (A Computerized Visual Model) --- p.32 / Chapter 3.3 --- Local orientated line extraction (Stage 1 of PSVM) --- p.34 / Chapter 3.3.1 --- Orientated line detection network --- p.35 / Chapter 3.3.2 --- On-type orientated lines and off-type orientated lines --- p.37 / Chapter 3.4 --- Local line matching (Stage 2 of PSVM) --- p.38 / Chapter 3.4.1 --- Structure of hypercolumn in PSVM --- p.39 / Chapter 3.4.2 --- Line length discrimination model (Part of stage 2 of PSVM) --- p.41 / Chapter 3.4.3 --- Orientation-length detector --- p.42 / Chapter 3.4.4 --- Line length selection --- p.45 / Chapter 3.4.5 --- The matching model --- p.46 / Chapter 3.4.6 --- Fusional area in PSVM --- p.48 / Chapter 3.4.7 --- Matching mechanism --- p.49 / Chapter 3.4.8 --- Disparity detection --- p.50 / Chapter 3.5 --- Disparity integrations (Stage 3 of PSVM) --- p.53 / Chapter 3.5.1 --- The voter network --- p.54 / Chapter 3.5.2 --- The redistributor network --- p.55 / Chapter 3.6 --- Conculsion of chpater 3 --- p.57 / Chapter Chapter4 --- Implementation and Analysis --- p.58 / Chapter 4.1 --- The imaging geometry of PSVM --- p.58 / Chapter 4.2 --- Input --- p.59 / Chapter 4.3 --- The hypercolumn construction --- p.59 / Chapter 4.4 --- Analysis of matching mechanism in PSVM --- p.59 / Chapter 4.4.1 --- Fusional condition --- p.61 / Chapter 4.4.2 --- Disparity detection --- p.61 / Chapter 4.5. --- Matching rules in PSVM --- p.63 / Chapter 4.5.1 --- The ordering constraint --- p.63 / Chapter 4.5.2 --- The uniqueness constraint --- p.64 / Chapter 4.5.3 --- The figural continuity constraint --- p.64 / Chapter 4.5.4 --- The smoothness assumption --- p.65 / Chapter 4.6. --- Use multi-lengths of oriented line to solve the occlusion problem --- p.66 / Chapter 4.7 --- Performance of PSVM --- p.67 / Chapter 4.7.1 --- Artificial scene --- p.67 / Chapter 4.7.2 --- Natural images --- p.71 / Chapter 4.8 --- Discussion --- p.83 / Chapter 4.9 --- Overall conclusion --- p.83 / Appendix: Illustration example --- p.85 / References --- p.91
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Tight frame based multi-focus image fusion with common degraded areas and upscaling via a single image. / CUHK electronic theses & dissertations collectionJanuary 2013 (has links)
Wang, Tianming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 59-62). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.
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From images to motion. / CUHK electronic theses & dissertations collectionJanuary 1998 (has links)
by Siu-hang Or. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (p. 94-100). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.
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3D model reconstruction with noise filtering using boundary edges.January 2004 (has links)
Lau Tak Fu. / Thesis submitted in: October 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 93-98). / Abstracts in English and Chinese. / Chapter 1 - --- Introduction --- p.9 / Chapter 1.1 --- Scope of the work --- p.9 / Chapter 1.2 --- Main contribution --- p.11 / Chapter 1.3 --- Outline of the thesis --- p.12 / Chapter 2 - --- Background --- p.14 / Chapter 2.1 --- Three dimensional models from images --- p.14 / Chapter 2.2 --- Un-calibrated 3D reconstruction --- p.14 / Chapter 2.3 --- Self calibrated 3D reconstruction --- p.16 / Chapter 2.4 --- Initial model formation using image based --- p.18 / Chapter 2.5 --- Volumes from Silhouettes --- p.19 / Chapter 3 - --- Initial model reconstruct the problem with mismatch noise --- p.22 / Chapter 3.1 --- Perspective Camera Model --- p.24 / Chapter 3.2 --- "Intrinsic parameters, Extrinsic parameters and camera motion" --- p.25 / Chapter 3.2.1 --- Intrinsic parameters --- p.25 / Chapter 3.2.2 --- Extrinsic parameter and camera motion --- p.27 / Chapter 3.3 --- Lowe's method --- p.29 / Chapter 3.4 --- Interleave bundle adjustment for structure and motion recovery from multiple images --- p.32 / Chapter 3.5 --- Feature points mismatch analysis --- p.38 / Chapter 4 - --- Feature selection by using look forward silhouette clipping --- p.43 / Chapter 4.1 --- Introduction to silhouette clipping --- p.43 / Chapter 4.2 --- Silhouette clipping for 3D model --- p.45 / Chapter 4.3 --- Implementation --- p.52 / Chapter 4.3.1 --- Silhouette extraction program --- p.52 / Chapter 4.3.2 --- Feature filter for alternative bundle adjustment algorithm --- p.59 / Chapter 5 - --- Experimental data --- p.61 / Chapter 5.1 --- Simulation --- p.61 / Chapter 5.1.1 --- Input of simulation --- p.61 / Chapter 5.1.2 --- Output of the simulation --- p.66 / Chapter 5.1.2.1 --- Radius distribution --- p.66 / Chapter 5.1.2.2 --- 3D model output --- p.74 / Chapter 5.1.2.3 --- VRML plotting --- p.80 / Chapter 5.2 --- Real Image testing --- p.82 / Chapter 5.2.1 --- Toy house on a turntable test --- p.82 / Chapter 5.2.2 --- Other tests on turntable --- p.86 / Chapter 6 - --- Conclusion and discussion --- p.89
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A learning-by-example method for reducing BDCT compression artifacts in high-contrast images.January 2004 (has links)
Wang, Guangyu. / Thesis submitted in: December 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 70-75). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- BDCT Compression Artifacts --- p.1 / Chapter 1.2 --- Previous Artifact Removal Methods --- p.3 / Chapter 1.3 --- Our Method --- p.4 / Chapter 1.4 --- Structure of the Thesis --- p.4 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Image Compression --- p.6 / Chapter 2.2 --- A Typical BDCT Compression: Baseline JPEG --- p.7 / Chapter 2.3 --- Existing Artifact Removal Methods --- p.10 / Chapter 2.3.1 --- Post-Filtering --- p.10 / Chapter 2.3.2 --- Projection onto Convex Sets --- p.12 / Chapter 2.3.3 --- Learning by Examples --- p.13 / Chapter 2.4 --- Other Related Work --- p.14 / Chapter 3 --- Contamination as Markov Random Field --- p.17 / Chapter 3.1 --- Markov Random Field --- p.17 / Chapter 3.2 --- Contamination as MRF --- p.18 / Chapter 4 --- Training Set Preparation --- p.22 / Chapter 4.1 --- Training Images Selection --- p.22 / Chapter 4.2 --- Bit Rate --- p.23 / Chapter 5 --- Artifact Vectors --- p.26 / Chapter 5.1 --- Formation of Artifact Vectors --- p.26 / Chapter 5.2 --- Luminance Remapping --- p.29 / Chapter 5.3 --- Dominant Implication --- p.29 / Chapter 6 --- Tree-Structured Vector Quantization --- p.32 / Chapter 6.1 --- Background --- p.32 / Chapter 6.1.1 --- Vector Quantization --- p.32 / Chapter 6.1.2 --- Tree-Structured Vector Quantization --- p.33 / Chapter 6.1.3 --- K-Means Clustering --- p.34 / Chapter 6.2 --- TSVQ in Artifact Removal --- p.35 / Chapter 7 --- Synthesis --- p.39 / Chapter 7.1 --- Color Processing --- p.39 / Chapter 7.2 --- Artifact Removal --- p.40 / Chapter 7.3 --- Selective Rejection of Synthesized Values --- p.42 / Chapter 8 --- Experimental Results --- p.48 / Chapter 8.1 --- Image Quality Assessments --- p.48 / Chapter 8.1.1 --- Peak Signal-Noise Ratio --- p.48 / Chapter 8.1.2 --- Mean Structural SIMilarity --- p.49 / Chapter 8.2 --- Performance --- p.50 / Chapter 8.3 --- How Size of Training Set Affects the Performance --- p.52 / Chapter 8.4 --- How Bit Rates Affect the Performance --- p.54 / Chapter 8.5 --- Comparisons --- p.56 / Chapter 9 --- Conclusion --- p.61 / Chapter A --- Color Transformation --- p.63 / Chapter B --- Image Quality --- p.64 / Chapter B.1 --- Image Quality vs. Quantization Table --- p.64 / Chapter B.2 --- Image Quality vs. Bit Rate --- p.66 / Chapter C --- Arti User's Manual --- p.68 / Bibliography --- p.70
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3D coarse-to-fine reconstruction from multiple image sequences.January 2004 (has links)
Ip Che Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 119-127). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Previous Work --- p.2 / Chapter 1.2.1 --- Reconstruction for Architecture Scene --- p.2 / Chapter 1.2.2 --- Super-resolution --- p.4 / Chapter 1.2.3 --- Coarse-to-Fine Approach --- p.4 / Chapter 1.3 --- Proposed solution --- p.6 / Chapter 1.4 --- Contribution --- p.6 / Chapter 1.5 --- Publications --- p.7 / Chapter 1.6 --- Layout of the thesis --- p.7 / Chapter 2 --- Background Techniques --- p.8 / Chapter 2.1 --- Interest Point Detectors --- p.8 / Chapter 2.1.1 --- Scale-space --- p.9 / Chapter 2.1.2 --- Harris Corner detectors --- p.10 / Chapter 2.1.3 --- Other Kinds of Interest Point Detectors --- p.17 / Chapter 2.1.4 --- Summary --- p.18 / Chapter 2.2 --- Steerable filters --- p.19 / Chapter 2.2.1 --- Orientation estimation --- p.20 / Chapter 2.3 --- Point Descriptors --- p.22 / Chapter 2.3.1 --- Image derivatives under illumination change --- p.23 / Chapter 2.3.2 --- Image derivatives under geometric scale change --- p.24 / Chapter 2.3.3 --- An example of a point descriptor --- p.25 / Chapter 2.3.4 --- Other examples --- p.25 / Chapter 2.4 --- Feature Tracking Techniques --- p.26 / Chapter 2.4.1 --- Kanade-Lucas-Tomasi (KLT) Tracker --- p.26 / Chapter 2.4.2 --- Guided Tracking Algorithm --- p.28 / Chapter 2.5 --- RANSAC --- p.29 / Chapter 2.6 --- Structure-from-motion (SFM) Algorithm --- p.31 / Chapter 2.6.1 --- Factorization methods --- p.33 / Chapter 2.6.2 --- Epipolar Geometry --- p.39 / Chapter 2.6.3 --- Bundle Adjustment --- p.47 / Chapter 2.6.4 --- Summary --- p.50 / Chapter 3 --- Hierarchical Registration of 3D Models --- p.52 / Chapter 3.1 --- Overview --- p.53 / Chapter 3.1.1 --- The Arrangement of Image Sequences --- p.53 / Chapter 3.1.2 --- The Framework --- p.54 / Chapter 3.2 3 --- D Model Reconstruction for Each Sequence --- p.57 / Chapter 3.3 --- Multi-scale Image Matching --- p.59 / Chapter 3.3.1 --- Scale-space interest point detection --- p.61 / Chapter 3.3.2 --- Point descriptor --- p.61 / Chapter 3.3.3 --- Point-to-point matching --- p.63 / Chapter 3.3.4 --- Image transformation estimation --- p.64 / Chapter 3.3.5 --- Multi-level image matching --- p.66 / Chapter 3.4 --- Linkage Establishment --- p.68 / Chapter 3.5 --- 3D Model Registration --- p.70 / Chapter 3.6 --- VRML Modelling --- p.73 / Chapter 4 --- Experiment --- p.74 / Chapter 4.1 --- Synthetic Experiments --- p.74 / Chapter 4.1.1 --- Study on Rematching Algorithm --- p.74 / Chapter 4.1.2 --- Comparison between Affine and Metric transforma- tions for 3D Registration --- p.80 / Chapter 4.2 --- Real Scene Experiments --- p.86 / Chapter 5 --- Conclusion --- p.112 / Chapter 5.1 --- Future Work --- p.114 / Chapter A --- Camera Parameters --- p.116 / Chapter A.1 --- Intrinsic Parameters --- p.116 / Chapter A.2 --- Extrinsic Parameters --- p.117 / Bibliography --- p.127
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Correspondence-free stereo vision.January 2004 (has links)
by Yuan, Ding. / Thesis submitted in: December 2003. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 69-71). / Abstracts in English and Chinese. / ABSTRACT --- p.i / 摘要 --- p.iii / ACKNOWLEDGEMENTS --- p.v / TABLE OF CONTENTS --- p.vi / LIST OF FIGURES --- p.viii / LIST OF TABLES --- p.xii / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 2 --- PREVIOUS WORK --- p.5 / Chapter 2.1 --- Traditional Stereo Vision --- p.5 / Chapter 2.1.1 --- Epipolar Constraint --- p.7 / Chapter 2.1.2 --- Some Constraints Based on Properties of Scene Objects --- p.9 / Chapter 2.1.3 --- Two Classes of Algorithms for Correspondence Establishment --- p.10 / Chapter 2.2 --- Correspondenceless Stereo Vision Algorithm for Single Planar Surface Recovery under Parallel-axis Stereo Geometry --- p.13 / Chapter 3 --- CORRESPONDENCE-FREE STEREO VISION UNDER GENERAL STEREO SETUP --- p.19 / Chapter 3.1 --- Correspondence-free Stereo Vision Algorithm for Single Planar Surface Recovery under General Stereo Geometry --- p.20 / Chapter 3.1.1 --- Algorithm in Its Basic Form --- p.21 / Chapter 3.1.2 --- Algorithm Combined with Epipolar Constraint --- p.25 / Chapter 3.1.3 --- Algorithm Combined with SVD And Robust Estimation --- p.36 / Chapter 3.2 --- Correspondence-free Stereo Vision Algorithm for Multiple Planar Surface Recovery --- p.45 / Chapter 3.2.1 --- Plane Hypothesis --- p.46 / Chapter 3.2.2 --- Plane Confirmation And 3D Reconstruction --- p.48 / Chapter 3.2.3 --- Experimental Results --- p.50 / Chapter 3.3 --- Experimental Results on Correspondence-free Vs. Correspondence Based Methods --- p.60 / Chapter 4 --- CONCLUSION AND FUTURE WORK --- p.65 / APPENDIX --- p.67 / BIBLIOGRAPHY --- p.69
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Edge-enhancing image smoothing.January 2011 (has links)
Xu, Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 62-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Organization --- p.4 / Chapter 2 --- Background and Motivation --- p.7 / Chapter 2.1 --- ID Mondrian Smoothing --- p.9 / Chapter 2.2 --- 2D Formulation --- p.13 / Chapter 3 --- Solver --- p.16 / Chapter 3.1 --- More Analysis --- p.20 / Chapter 4 --- Edge Extraction --- p.26 / Chapter 4.1 --- Related work --- p.26 / Chapter 4.2 --- Method and Results --- p.28 / Chapter 4.3 --- Summary --- p.32 / Chapter 5 --- Image Abstraction and Pencil Sketching --- p.35 / Chapter 5.1 --- Related Work --- p.35 / Chapter 5.2 --- Method and Results --- p.36 / Chapter 5.3 --- Summary --- p.40 / Chapter 6 --- Clip-Art Compression Artifact Removal --- p.41 / Chapter 6.1 --- Related work --- p.41 / Chapter 6.2 --- Method and Results --- p.43 / Chapter 6.3 --- Summary --- p.46 / Chapter 7 --- Layer-Based Contrast Manipulation --- p.49 / Chapter 7.1 --- Related Work --- p.49 / Chapter 7.2 --- Method and Results --- p.50 / Chapter 7.2.1 --- Edge Adjustment --- p.51 / Chapter 7.2.2 --- Detail Magnification --- p.54 / Chapter 7.2.3 --- Tone Mapping --- p.55 / Chapter 7.3 --- Summary --- p.56 / Chapter 8 --- Conclusion and Discussion --- p.59 / Bibliography --- p.61
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