Template Matching Techniques in Computer Vision -- CONTENTS -- Preface -- 1 Introduction -- 1.1 Template Matching and Computer Vision -- 1.2 The Book -- 1.3 Bibliographical Remarks -- References -- 2 The Imaging Process -- 2.1 Image Creation -- 2.1.1 Light -- 2.1.2 Gathering Light -- 2.1.3 Diffraction-limited Systems -- 2.1.4 Quantum Noise -- 2.2 Biological Eyes -- 2.2.1 The Human Eye -- 2.2.2 Alternative Designs -- 2.3 Digital Eyes -- 2.4 Digital Image Representations -- 2.4.1 The Sampling Theorem -- 2.4.2 Image Resampling -- 2.4.3 Log-polar Mapping -- 2.5 Bibliographical Remarks -- References -- 3 Template Matching as Testing -- 3.1 Detection and Estimation -- 3.2 Hypothesis Testing -- 3.2.1 The Bayes Risk Criterion -- 3.2.2 The Neyman-Pearson Criterion -- 3.3 An Important Example -- 3.4 A Signal Processing Perspective: Matched Filters -- 3.5 Pattern Variability and the Normalized Correlation Coefficient -- 3.6 Estimation -- 3.6.1 Maximum Likelihood Estimation -- 3.6.2 Bayes Estimation -- 3.6.3 James-Stein Estimation -- 3.7 Bibliographical Remarks -- References -- 4 Robust Similarity Estimators -- 4.1 Robustness Measures -- 4.2 M-estimators -- 4.3 L1 Similarity Measures -- 4.4 Robust Estimation of Covariance Matrices -- 4.5 Bibliographical Remarks -- References -- 5 Ordinal Matching Measures -- 5.1 Ordinal Correlation Measures -- 5.1.1 Spearman Rank Correlation -- 5.1.2 Kendall Correlation -- 5.1.3 Bhat-Nayar Correlation -- 5.2 Non-parametric Local Transforms -- 5.2.1 The Census and Rank Transforms -- 5.2.2 Incremental Sign Correlation -- 5.3 Bibliographical Remarks -- References -- 6 Matching Variable Patterns -- 6.1 Multiclass Synthetic Discriminant Functions -- 6.2 Advanced Synthetic Discriminant Functions -- 6.3 Non-orthogonal Image Expansion -- 6.4 Bibliographical Remarks -- References -- 7 Matching Linear Structure: The Hough Transform.

7.1 Getting Shapes: Edge Detection -- 7.2 The Radon Transform -- 7.3 The Hough Transform: Line and Circle Detection -- 7.4 The Generalized Hough Transform -- 7.5 Bibliographical Remarks -- References -- 8 Low-dimensionality Representations and Matching -- 8.1 Principal Components -- 8.1.1 Probabilistic PCA -- 8.1.2 How Many Components? -- 8.2 A Nonlinear Approach: Kernel PCA -- 8.3 Independent Components -- 8.4 Linear Discriminant Analysis -- 8.4.1 Bayesian Dual Spaces -- 8.5 A Sample Application: Photographic-quality Facial Composites -- 8.6 Bibliographical Remarks -- References -- 9 Deformable Templates -- 9.1 A Dynamic Perspective on the Hough Transform -- 9.2 Deformable Templates -- 9.3 Active Shape Models -- 9.4 Diffeomorphic Matching -- 9.5 Bibliographical Remarks -- References -- 10 Computational Aspects of Template Matching -- 10.1 Speed -- 10.1.1 Early Jump-out -- 10.1.2 The Use of Sum Tables -- 10.1.3 Hierarchical Template Matching -- 10.1.4 Metric Inequalities -- 10.1.5 The FFT Advantage -- 10.1.6 PCA-based Speed-up -- 10.1.7 A Combined Approach -- 10.2 Precision -- 10.2.1 A Perturbative Approach -- 10.2.2 Phase Correlation -- 10.3 Bibliographical Remarks -- References -- 11 Matching Point Sets: The Hausdorff Distance -- 11.1 Metric Pattern Spaces -- 11.2 Hausdorff Matching -- 11.3 Efficient Computation of the Hausdorff Distance -- 11.4 Partial Hausdorff Matching -- 11.5 Robustness Aspects -- 11.6 A Probabilistic Perspective -- 11.7 Invariant Moments -- 11.8 Bibliographical Remarks -- References -- 12 Support Vector Machines and Regularization Networks -- 12.1 Learning and Regularization -- 12.2 RBF Networks -- 12.2.1 RBF Networks for Gender Recognition -- 12.3 Support Vector Machines -- 12.3.1 Improving Efficiency -- 12.3.2 Multiclass SVMs -- 12.3.3 Best Practice -- 12.4 Bibliographical Remarks -- References -- 13 Feature Templates.

13.1 Detecting Templates by Features -- 13.2 Parametric Feature Manifolds -- 13.3 Multiclass Pattern Rejection -- 13.4 Template Features -- 13.5 Bibliographical Remarks -- References -- 14 Building a Multibiometric System -- 14.1 Systems -- 14.2 The Electronic Librarian -- 14.3 Score Integration -- 14.4 Rejection -- 14.5 Bibliographical Remarks -- References -- Appendices -- A AnImAl: A Software Environment for Fast Prototyping -- A.1 AnImAl: An Image Algebra -- A.2 Image Representation and Processing Abstractions -- A.3 The AnImAl Environment -- A.4 Bibliographical Remarks -- References -- B Synthetic Oracles for Algorithm Development -- B.1 Computer Graphics -- B.2 Describing Reality: Flexible Rendering Languages -- B.3 Bibliographical Remarks -- References -- C On Evaluation -- C.1 A Note on Performance Evaluation -- C.2 Training a Classifier -- C.3 Analyzing the Performance of a Classifier -- C.4 Evaluating a Technology -- C.5 Bibliographical Remarks -- References -- Index.