Image representation, processing and analysis by support vector regression.

• Other Authors: Chow, Kai Tik.
• Format: Others
• Language: English; Chinese
• Published: 2001
• Subjects: Image processing; Machine learning; Kernel functions
• Online Access: http://library.cuhk.edu.hk/record=b5890679; http://repository.lib.cuhk.edu.hk/en/item/cuhk-323534

Chow Kai Tik = 支援矢量回歸法之影像表示式及其影像處理與分析 / 周啓迪. === Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. === Includes bibliographical references (leaves 380-383). === Text in English; abstracts in English and Chinese. === Chow Kai Tik = Zhi yuan shi liang hui gui fa zhi ying xiang biao shi shi ji qi ying xiang chu li yu fen xi / Zhou Qidi. === Abstract in English === Abstract in Chinese === Acknowledgement === Content === List of figures === Chapter Chapter 1 --- Introduction --- p.1-11 === Chapter 1.1 --- Introduction --- p.2 === Chapter 1.2 --- Road Map --- p.9 === Chapter Chapter 2 --- Review of Support Vector Machine --- p.12-124 === Chapter 2.1 --- Structural Risk Minimization (SRM) --- p.13 === Chapter 2.1.1 --- Introduction === Chapter 2.1.2 --- Structural Risk Minimization === Chapter 2.2 --- Review of Support Vector Machine --- p.21 === Chapter 2.2.1 --- Review of Support Vector Classification === Chapter 2.2.2 --- Review of Support Vector Regression === Chapter 2.2.3 --- Review of Support Vector Clustering === Chapter 2.2.4 --- Summary of Support Vector Machines === Chapter 2.3 --- Implementation of Support Vector Machines --- p.60 === Chapter 2.3.1 --- Kernel Adatron for Support Vector Classification (KA-SVC) === Chapter 2.3.2 --- Kernel Adatron for Support Vector Regression (KA-SVR) === Chapter 2.3.3 --- Sequential Minimal Optimization for Support Vector Classification (SMO-SVC) === Chapter 2.3.4 --- Sequential Minimal Optimization for Support Vector Regression (SMO-SVR) === Chapter 2.3.5 --- Lagrangian Support Vector Classification (LSVC) === Chapter 2.3.6 --- Lagrangian Support Vector Regression (LSVR) === Chapter 2.4 --- Applications of Support Vector Machines --- p.117 === Chapter 2.4.1 --- Applications of Support Vector Classification === Chapter 2.4.2 --- Applications of Support Vector Regression === Chapter Chapter 3 --- Image Representation by Support Vector Regression --- p.125-183 === Chapter 3.1 --- Introduction of SVR Representation --- p.116 === Chapter 3.1.1 --- Image Representation by SVR === Chapter 3.1.2 --- Implicit Smoothing of SVR representation === Chapter 3.1.3 --- "Different Insensitivity, C value, Kernel and Kernel Parameters" === Chapter 3.2 --- Variation on Encoding Method [Training Process] --- p.154 === Chapter 3.2.1 --- Training SVR with Missing Data === Chapter 3.2.2 --- Training SVR with Image Blocks === Chapter 3.2.3 --- Training SVR with Other Variations === Chapter 3.3 --- Variation on Decoding Method [Testing pr Reconstruction Process] --- p.171 === Chapter 3.3.1 --- Reconstruction with Different Portion of Support Vectors === Chapter 3.3.2 --- Reconstruction with Different Support Vector Locations and Lagrange Multiplier Values === Chapter 3.3.3 --- Reconstruction with Different Kernels === Chapter 3.4 --- Feature Extraction --- p.177 === Chapter 3.4.1 --- Features on Simple Shape === Chapter 3.4.2 --- Invariant of Support Vector Features === Chapter Chapter 4 --- Mathematical and Physical Properties of SYR Representation --- p.184-243 === Chapter 4.1 --- Introduction of RBF Kernel --- p.185 === Chapter 4.2 --- Mathematical Properties: Integral Properties --- p.187 === Chapter 4.2.1 --- Integration of an SVR Image === Chapter 4.2.2 --- Fourier Transform of SVR Image (Hankel Transform of Kernel) === Chapter 4.2.3 --- Cross Correlation between SVR Images === Chapter 4.2.4 --- Convolution of SVR Images === Chapter 4.3 --- Mathematical Properties: Differential Properties --- p.219 === Chapter 4.3.1 --- Review of Differential Geometry === Chapter 4.3.2 --- Gradient of SVR Image === Chapter 4.3.3 --- Laplacian of SVR Image === Chapter 4.4 --- Physical Properties --- p.228 === Chapter 4.4.1 --- 7Transformation between Reconstructed Image and Lagrange Multipliers === Chapter 4.4.2 --- Relation between Original Image and SVR Approximation === Chapter 4.5 --- Appendix --- p.234 === Chapter 4.5.1 --- Hankel Transform for Common Functions === Chapter 4.5.2 --- Hankel Transform for RBF === Chapter 4.5.3 --- Integration of Gaussian === Chapter 4.5.4 --- Chain Rules for Differential Geometry === Chapter 4.5.5 --- Derivation of Gradient of RBF === Chapter 4.5.6 --- Derivation of Laplacian of RBF === Chapter Chapter 5 --- Image Processing in SVR Representation --- p.244-293 === Chapter 5.1 --- Introduction --- p.245 === Chapter 5.2 --- Geometric Transformation --- p.241 === Chapter 5.2.1 --- "Brightness, Contrast and Image Addition" === Chapter 5.2.2 --- Interpolation or Resampling === Chapter 5.2.3 --- Translation and Rotation === Chapter 5.2.4 --- Affine Transformation === Chapter 5.2.5 --- Transformation with Given Optical Flow === Chapter 5.2.6 --- A Brief Summary === Chapter 5.3 --- SVR Image Filtering --- p.261 === Chapter 5.3.1 --- Discrete Filtering in SVR Representation === Chapter 5.3.2 --- Continuous Filtering in SVR Representation === Chapter Chapter 6 --- Image Analysis in SVR Representation --- p.294-370 === Chapter 6.1 --- Contour Extraction --- p.295 === Chapter 6.1.1 --- Contour Tracing by Equi-potential Line [using Gradient] === Chapter 6.1.2 --- Contour Smoothing and Contour Feature Extraction === Chapter 6.2 --- Registration --- p.304 === Chapter 6.2.1 --- Registration using Cross Correlation === Chapter 6.2.2 --- Registration using Phase Correlation [Phase Shift in Fourier Transform] === Chapter 6.2.3 --- Analysis of the Two Methods for Registrationin SVR Domain === Chapter 6.3 --- Segmentation --- p.347 === Chapter 6.3.1 --- Segmentation by Contour Tracing === Chapter 6.3.2 --- Segmentation by Thresholding on Smoothed or Sharpened SVR Image === Chapter 6.3.3 --- Segmentation by Thresholding on SVR Approximation === Chapter 6.4 --- Appendix --- p.368 === Chapter Chapter 7 --- Conclusion --- p.371-379 === Chapter 7.1 --- Conclusion and contribution --- p.372 === Chapter 7.2 --- Future work --- p.378 === Reference --- p.380-383