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 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

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

To Kin Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 99-104). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Multi-carrier Communication Systems --- p.1 / Chapter 1.2 --- Objective of the Thesis --- p.4 / Chapter 1.3 --- Thesis Outline --- p.5 / Chapter 2 --- Wavelet Packet Division Multiplexing (WPDM) --- p.7 / Chapter 2.1 --- "Wavelets, Wavelet Packets and Multiresolution Analysis (MRA)" --- p.8 / Chapter 2.2 --- Application of Wavelet Packets in Multiple Signal Transmission --- p.14 / Chapter 2.3 --- Summary --- p.20 / Chapter 3 --- Nonlinear System Theories --- p.22 / Chapter 3.1 --- Characteristics of Memoryless Nonlinearities --- p.23 / Chapter 3.1.1 --- Memoryless Baseband Nonlinearities --- p.23 / Chapter 3.1.2 --- Memoryless Bandpass Nonlinearities --- p.24 / Chapter 3.2 --- Volt err a Series for Nonlinearities with Memory --- p.26 / Chapter 3.2.1 --- Baseband Nonlinearities with Memory --- p.26 / Chapter 3.2.2 --- Bandpass Nonlinearities with Memory --- p.27 / Chapter 3.3 --- High Power Amplifier (HPA) Models --- p.28 / Chapter 3.3.1 --- Traveling Wave Tube Amplifier (TWTA) --- p.28 / Chapter 3.3.2 --- Solid State Power Amplifier (SSPA) --- p.28 / Chapter 3.3.3 --- Input and Output Backoff Ratios --- p.29 / Chapter 3.4 --- Summary --- p.29 / Chapter 4 --- WPDM in the Presence of Amplifier Nonlinearities --- p.30 / Chapter 4.1 --- System Model --- p.31 / Chapter 4.2 --- Derivation of Channel Models --- p.32 / Chapter 4.2.1 --- Single-carrier WPDM --- p.32 / Chapter 4.2.2 --- Multi-carrier WPDM --- p.34 / Chapter 4.3 --- Performance Analysis --- p.35 / Chapter 4.3.1 --- Conditional Mean --- p.36 / Chapter 4.3.2 --- Conditional Variance --- p.41 / Chapter 4.3.3 --- Power Spectral Density (PSD) --- p.44 / Chapter 4.4 --- Probability of Symbol Error --- p.48 / Chapter 4.5 --- Simulation Results --- p.52 / Chapter 4.6 --- Summary --- p.56 / Chapter 5 --- Nonlinear Compensation (The pth-Order Inverse) --- p.57 / Chapter 5.1 --- Data Predistortion --- p.58 / Chapter 5.2 --- Predistorter Structure --- p.62 / Chapter 5.3 --- Complexity --- p.63 / Chapter 5.4 --- Simulation Results --- p.66 / Chapter 5.5 --- Summary --- p.78 / Chapter 6 --- Conclusions and Suggestions for Future Research --- p.79 / Chapter 6.1 --- Conclusions --- p.79 / Chapter 6.2 --- Suggestions for Future Research --- p.82 / Appendices --- p.83 / Chapter A --- Derivation of (4.14) --- p.83 / Chapter B --- Derivation of (4.16) --- p.85 / Chapter C --- Evaluation of higher order expectations --- p.86 / Chapter D --- Derivation of φ ss(T) in (4.32) --- p.90 / Chapter E --- Derivation of φsi(T) in(4.32) --- p.93 / Chapter F --- Derivation of φ is(T) in (4.32) --- p.95 / Chapter G --- Derivation of φii(T) in (4.32) --- p.97 / Bibliography --- p.99

我们提出了两个新的方法来解决低级别计算机视觉任务，即图像共同分割和降噪。 / 在共同分割模型上，我们发现对象对应可以为前景统计估计提供有用的信息。我们的方法可以处理极具挑战性的场景，如变形，角度的变化和显着不同的视角和尺度。此外，我们研究了一种新的能量最小化模型，可以同时处理多个图像。真实和基准数据的定性和定量实验证明该方法的有效性。 / 另一方面，噪音始终和高频图像结构是紧耦合的，从而使得减少噪音非常很难。在我们的降噪模型中，我们建议稍微使图像光学离焦，以减少图像和噪声的耦合。这使得我们能更有效地降低噪音，随后恢复失焦。我们的分析显示，这是可能的，并且用许多例子证明我们的技术，其中包括低光图像。 / We present two novel methods to tackle low level computer vision tasks,i.e., image cosegmentation and denoise . / In our cosegmentationmodel, we discover object correspondence canprovide useful information for foreground statistical estimation. Ourmethod can handle extremely challenging scenarios such as deformation, perspective changes and dramatically different viewpoints/scales. In addition, we develop a novel energy minimization model that can handlemultiple images. Experiments on real and benchmark data qualitatively and quantitatively demonstrate the effectiveness of the approach. / One the other hand, noise is always tightly coupled with high-frequencyimage structure, making noise reduction generally very difficult. In ourdenoise model, we propose slightly optically defocusing the image in orderto loosen this noise-image structure coupling. This allows us to more effectively reduce noise and subsequently restore the small defocus. Weanalytically show how this is possible, and demonstrate our technique on a number of examples that include low-light images. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Qin, Zenglu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 64-71). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.1.1 --- Cosegmentation --- p.1 / Chapter 1.1.2 --- Image Denoise --- p.4 / Chapter 1.2 --- Thesis Outline --- p.7 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Cosegmentation --- p.8 / Chapter 2.2 --- Image Denoise --- p.10 / Chapter 3 --- Cosegmentation of Multiple Deformable Objects --- p.12 / Chapter 3.1 --- Related Work --- p.12 / Chapter 3.2 --- Object Corresponding Cosegmentation --- p.13 / Chapter 3.3 --- Importance Map with Object Correspondence --- p.15 / Chapter 3.3.1 --- Feature Importance Map --- p.16 / Chapter 3.3.2 --- Importance Energy E[subscript i](xp) --- p.20 / Chapter 3.4 --- Experimental Result --- p.20 / Chapter 3.4.1 --- Two-Image Cosegmentation --- p.21 / Chapter 3.4.2 --- ETHZ Toys Dataset --- p.22 / Chapter 3.4.3 --- More Results --- p.24 / Chapter 3.5 --- Summary --- p.27 / Chapter 4 --- Using Optical Defocus to Denoise --- p.28 / Chapter 4.1 --- Related Work --- p.29 / Chapter 4.2 --- Noise Analysis --- p.30 / Chapter 4.3 --- Noise Estimation with Focal Blur --- p.33 / Chapter 4.3.1 --- Noise Estimation with a Convolution Model --- p.33 / Chapter 4.3.2 --- Determining λ --- p.41 / Chapter 4.4 --- Final Deconvolution and Error Analysis --- p.43 / Chapter 4.5 --- Implementation --- p.45 / Chapter 4.6 --- Quantitative Evaluation --- p.47 / Chapter 4.7 --- More Experimental Results --- p.53 / Chapter 4.8 --- Summary --- p.56 / Chapter 5 --- Conclusion --- p.62 / Bibliography --- p.64

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography: leaves 50-57. / by Paul S. Heckbert. / B.S.

Yuan Meng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 85-91). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- History of ASR development --- p.2 / Chapter 1.2 --- Fundamentals of automatic speech recognition --- p.3 / Chapter 1.2.1 --- Classification of ASR systems --- p.3 / Chapter 1.2.2 --- Automatic speech recognition process --- p.4 / Chapter 1.3 --- Performance measurements of ASR --- p.7 / Chapter 1.3.1 --- Recognition accuracy --- p.7 / Chapter 1.3.2 --- Complexity --- p.7 / Chapter 1.3.3 --- Robustness --- p.8 / Chapter 1.4 --- Motivation and goal of this work --- p.8 / Chapter 1.5 --- Thesis outline --- p.10 / Chapter 2 --- Signal processing techniques for front-end --- p.12 / Chapter 2.1 --- Basic feature extraction principles --- p.13 / Chapter 2.1.1 --- Pre-emphasis --- p.13 / Chapter 2.1.2 --- Frame blocking and windowing --- p.13 / Chapter 2.1.3 --- Discrete Fourier Transform (DFT) computation --- p.15 / Chapter 2.1.4 --- Spectral magnitudes --- p.15 / Chapter 2.1.5 --- Mel-frequency filterbank --- p.16 / Chapter 2.1.6 --- Logarithm of filter energies --- p.18 / Chapter 2.1.7 --- Discrete Cosine Transformation (DCT) --- p.18 / Chapter 2.1.8 --- Cepstral Weighting --- p.19 / Chapter 2.1.9 --- Dynamic featuring --- p.19 / Chapter 2.2 --- Practical issues --- p.20 / Chapter 2.2.1 --- Review of practical problems and solutions in ASR appli- cations --- p.20 / Chapter 2.2.2 --- Model of environment --- p.23 / Chapter 2.2.3 --- End-point detection (EPD) --- p.23 / Chapter 2.2.4 --- Spectral subtraction (SS) --- p.25 / Chapter 3 --- HMM-based Acoustic Modeling --- p.26 / Chapter 3.1 --- HMMs for ASR --- p.26 / Chapter 3.2 --- Output probabilities --- p.27 / Chapter 3.3 --- Viterbi search engine --- p.29 / Chapter 3.4 --- Isolated word recognition (IWR) & Connected word recognition (CWR) --- p.30 / Chapter 3.4.1 --- Isolated word recognition --- p.30 / Chapter 3.4.2 --- Connected word recognition (CWR) --- p.31 / Chapter 4 --- DSP for embedded applications --- p.32 / Chapter 4.1 --- "Classification of embedded systems (DSP, ASIC, FPGA, etc.)" --- p.32 / Chapter 4.2 --- Description of hardware platform --- p.34 / Chapter 4.3 --- I/O operation for real-time processing --- p.36 / Chapter 4.4 --- Fixed point algorithm on DSP --- p.40 / Chapter 5 --- ASR algorithm optimization --- p.42 / Chapter 5.1 --- Methodology --- p.42 / Chapter 5.2 --- Floating-point to fixed-point conversion --- p.43 / Chapter 5.3 --- Computational complexity consideration --- p.45 / Chapter 5.3.1 --- Feature extraction techniques --- p.45 / Chapter 5.3.2 --- Viterbi search module --- p.50 / Chapter 5.4 --- Memory requirements consideration --- p.51 / Chapter 6 --- Experimental results and performance analysis --- p.53 / Chapter 6.1 --- Cantonese isolated word recognition (IWR) --- p.54 / Chapter 6.1.1 --- Execution time --- p.54 / Chapter 6.1.2 --- Memory requirements --- p.57 / Chapter 6.1.3 --- Recognition performance --- p.57 / Chapter 6.2 --- Connected word recognition (CWR) --- p.61 / Chapter 6.2.1 --- Execution time consideration --- p.62 / Chapter 6.2.2 --- Recognition performance --- p.62 / Chapter 6.3 --- Summary & discussion --- p.66 / Chapter 7 --- Implementation of practical techniques --- p.67 / Chapter 7.1 --- End-point detection (EPD) --- p.67 / Chapter 7.2 --- Spectral subtraction (SS) --- p.71 / Chapter 7.3 --- Experimental results --- p.72 / Chapter 7.3.1 --- Isolated word recognition (IWR) --- p.72 / Chapter 7.3.2 --- Connected word recognition (CWR) --- p.75 / Chapter 7.4 --- Results --- p.77 / Chapter 8 --- Conclusions and future work --- p.78 / Chapter 8.1 --- Summary and Conclusions --- p.78 / Chapter 8.2 --- Suggestions for future research --- p.80 / Appendices --- p.82 / Chapter A --- "Interpolation of data entries without floating point, divides or conditional branches" --- p.82 / Chapter B --- Vocabulary for Cantonese isolated word recognition task --- p.84 / Bibliography --- p.85