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

Graduate

abstract / Computer Science / Doctoral / Doctor of Philosophy

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

The problems of noise removal, and simultaneous noise removal and deblurring of imagery are common to many areas of science. An approach which allows for the unified treatment of both problems involves modeling imagery as a sample of a random process. Various nonstationary image models are explored in this context. Attention is directed to identifying the model parameters from imagery which has been corrupted by noise and possibly blur, and the use of the model to form an optimal reconstruction of the image. Throughout the work, emphasis is placed on both theoretical development and practical considerations involved in achieving this reconstruction. The results indicate that the use of nonstationary image models offers considerable improvement over traditional techniques.

This paper proposes a metric to predict edge detection performance when applied to an image with noise. First, models of edges and edge detection linear operators are characterized by their spatial and Fourier domain properties. Second, additive uncorrelated noise on the operator is examined and a metric is developed using the image formation system modulation transfer function (MTF), expected noise power spectral density, and edge detector characterization as inputs. Thirdly, the problem of partially correlated noise is examined. A separate performance metric for simple thresholded operator outputs is proposed. Finally, several discrete edge detectors in noise are evaluated numerically. Both the metric based on signal to noise detector output, and based on thresholding probabilities were useful in predicting previously published performance results. This was true even for many nonlinear detectors based on the linear detectors evaluated here. The specification of a localization criteria was critical for comparisons between detectors.

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

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

Image segmentation is about how an image could be divided into pieces or segments so that each segment corresponds to a surface or an object which demonstrates high degree of uniformity in its visual appearance. Automatic image segmentation method must in a way make use of the uniformity property of the segments. The uniformity property of a segment however manifests in a number of ways, forming different channels of features. Since the segment's appearance is uniform within itself, the intensities or textures in the image of the segment must look rather similar, and they together are what the literature calls the region-level features. Since a segment's appearance uniformity must be different from those of the immediate neighboring segments, or else they should not be referred to as different segments, the boundary of a segment therefore should exhibit high degree of intensity contrast in the image data, and such contrast leads to edgel features (which is often referred to as boundary features) in the image data. Other channels of features include discrete labels assigned to image pixels according to their intensity levels, and certain prior knowledge of the object shape in the image. / In the first piece of work, an approach of gray level image segmentation is investigated, which uses boundary feature and region feature complementarity. In this approach, the line segments, which are derived by grouping edge elements, are used to construct a saliency map to indicate the location likelihood of the real boundaries. The closed boundaries extracted from the saliency map are then refined by a region based active contour method. The scheme allows the challenging issues of boundary closure and segmentation accuracy to be both addressed. / In the second piece of work, an approach of foreground-background segmentation is explored, which integrates the boundary features and certain labels assigned to the image pixels according to their intensity levels. The labels are in accordance with certain coarse clustering over the intensity histogram of the image. In this approach, an inhomogeneity measure is encoded in a variational formulation, thus the measure can be applied to the entire image domain and be made global. The approach has a uniform treatment to gray level, color and texture images. In addition, the approach allows explicit encouragement on the smoothness of the segmentation boundary by using the level set technique-based active contour method. / In the third piece of work, an approach of foreground-background segmentation is investigated, that makes use of both the boundary features and certain prior knowledge of object shape. This approach can also be categorized as an object detection method. In this approach, we adopt a new multiplicative formulation to combine the edgel information and the prior shape knowledge. The method reduces the number of system parameters and increases the algorithm's robustness. / Much of the previous work on image segmentation is based upon features of a particular channel, such as the edgel features, the region features, or others. The objective of this thesis is to explore how features of multiple channels can be put together integratively, or more precisely in an active contour deformation process under the level set formulation, for more accurate image segmentation. Three combinations of features are investigated. The first is about integrating the boundary features and region features. The second is about integrating the boundary features and the labels to pixels according to certain coarse intensity clustering. The third is about integrating the boundary features and certain prior knowledge of the object shape. / The proposed algorithms in this thesis have been tested on many real and synthetic images. The experimental results illustrate their efficacy and limitation. / Wang, Wei. / "September 2007." / Adviser: Ronald Chi-kit Chung. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 4865. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 39-106). / 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. / Abstracts in English and Chinese. / School code: 1307.

Lam Yuet Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 80-86). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.1.1 --- Difficulties of fixed-point design --- p.1 / Chapter 1.1.2 --- Why still fixed-point? --- p.2 / Chapter 1.1.3 --- Difficulties of converting floating-point to fixed-point --- p.2 / Chapter 1.1.4 --- Why wordlength optimization? --- p.3 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- Review --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Simulation approach to address quantization issue --- p.6 / Chapter 2.3 --- Analytical approach to address quantization issue --- p.8 / Chapter 2.4 --- Implementation of speech systems --- p.9 / Chapter 2.5 --- Discussion --- p.10 / Chapter 2.6 --- Summary --- p.11 / Chapter 3 --- Fixed-point arithmetic background --- p.12 / Chapter 3.1 --- Introduction --- p.12 / Chapter 3.2 --- Fixed-point representation --- p.12 / Chapter 3.3 --- Fixed-point addition/subtraction --- p.14 / Chapter 3.4 --- Fixed-point multiplication --- p.16 / Chapter 3.5 --- Fixed-point division --- p.18 / Chapter 3.6 --- Summary --- p.20 / Chapter 4 --- Fixed-point class implementation --- p.21 / Chapter 4.1 --- Introduction --- p.21 / Chapter 4.2 --- Fixed-point simulation using overloading --- p.21 / Chapter 4.3 --- Fixed-point class implementation --- p.24 / Chapter 4.3.1 --- Fixed-point object declaration --- p.24 / Chapter 4.3.2 --- Overload the operators --- p.25 / Chapter 4.3.3 --- Arithmetic operations --- p.26 / Chapter 4.3.4 --- Automatic monitoring of dynamic range --- p.27 / Chapter 4.3.5 --- Automatic calculation of quantization error --- p.27 / Chapter 4.3.6 --- Array supporting --- p.28 / Chapter 4.3.7 --- Cosine calculation --- p.28 / Chapter 4.4 --- Summary --- p.29 / Chapter 5 --- Speech recognition background --- p.30 / Chapter 5.1 --- Introduction --- p.30 / Chapter 5.2 --- Isolated word recognition system overview --- p.30 / Chapter 5.3 --- Linear predictive coding processor --- p.32 / Chapter 5.3.1 --- The LPC model --- p.32 / Chapter 5.3.2 --- The LPC processor --- p.33 / Chapter 5.4 --- Vector quantization --- p.36 / Chapter 5.5 --- Hidden Markov model --- p.38 / Chapter 5.6 --- Summary --- p.40 / Chapter 6 --- Optimization --- p.41 / Chapter 6.1 --- Introduction --- p.41 / Chapter 6.2 --- Simplex Method --- p.41 / Chapter 6.2.1 --- Initialization --- p.42 / Chapter 6.2.2 --- Reflection --- p.42 / Chapter 6.2.3 --- Expansion --- p.44 / Chapter 6.2.4 --- Contraction --- p.44 / Chapter 6.2.5 --- Stop --- p.45 / Chapter 6.3 --- One-dimensional optimization approach --- p.45 / Chapter 6.3.1 --- One-dimensional optimization approach --- p.46 / Chapter 6.3.2 --- Search space reduction --- p.47 / Chapter 6.3.3 --- Speeding up convergence --- p.48 / Chapter 6.4 --- Summary --- p.50 / Chapter 7 --- Word Recognition System Design Methodology --- p.51 / Chapter 7.1 --- Introduction --- p.51 / Chapter 7.2 --- Framework design --- p.51 / Chapter 7.2.1 --- Fixed-point class --- p.52 / Chapter 7.2.2 --- Fixed-point application --- p.53 / Chapter 7.2.3 --- Optimizer --- p.53 / Chapter 7.3 --- Speech system implementation --- p.54 / Chapter 7.3.1 --- Model training --- p.54 / Chapter 7.3.2 --- Simulate the isolated word recognition system --- p.56 / Chapter 7.3.3 --- Hardware cost model --- p.57 / Chapter 7.3.4 --- Cost function --- p.58 / Chapter 7.3.5 --- Fraction size optimization --- p.59 / Chapter 7.3.6 --- One-dimensional optimization --- p.61 / Chapter 7.4 --- Summary --- p.63 / Chapter 8 --- Results --- p.64 / Chapter 8.1 --- Model training --- p.64 / Chapter 8.2 --- Simplex method optimization --- p.65 / Chapter 8.2.1 --- Simulation platform --- p.65 / Chapter 8.2.2 --- System level optimization --- p.66 / Chapter 8.2.3 --- LPC processor optimization --- p.67 / Chapter 8.2.4 --- One-dimensional optimization --- p.68 / Chapter 8.3 --- Speeding up the optimization convergence --- p.71 / Chapter 8.4 --- Optimization criteria --- p.73 / Chapter 8.5 --- Summary --- p.75 / Chapter 9 --- Conclusion --- p.76 / Chapter 9.1 --- Search space reduction --- p.76 / Chapter 9.2 --- Speeding up the searching --- p.77 / Chapter 9.3 --- Optimization criteria --- p.77 / Chapter 9.4 --- Flexibility of the framework design --- p.78 / Chapter 9.5 --- Further development --- p.78 / Bibliography --- p.80