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 Estimation3.2 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
7.1 Getting Shapes: Edge Detection7.2 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 Transform9.2 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
Summary
The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human percept
