{"product_id":"template-matching-techniques-in-computer-vision-theory-and-practice-9780470517062","title":"Template Matching Techniques in Computer Vision","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe 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 perception mechanisms, and for the development of practical biometric systems.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003e1 Introduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Template Matching and Computer Vision.\u003c\/p\u003e \u003cp\u003e1.2 The Book.\u003c\/p\u003e \u003cp\u003e1.3 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Imaging Process.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Image Creation.\u003c\/p\u003e \u003cp\u003e2.1.1 Light.\u003c\/p\u003e \u003cp\u003e2.1.2 Gathering Light.\u003c\/p\u003e \u003cp\u003e2.1.3 Diffraction-limited Systems.\u003c\/p\u003e \u003cp\u003e2.1.4 Quantum Noise.\u003c\/p\u003e \u003cp\u003e2.2 Biological Eyes.\u003c\/p\u003e \u003cp\u003e2.2.1 The Human Eye.\u003c\/p\u003e \u003cp\u003e2.2.2 Alternative Designs.\u003c\/p\u003e \u003cp\u003e2.3 Digital Eyes.\u003c\/p\u003e \u003cp\u003e2.4 Digital Image Representations.\u003c\/p\u003e \u003cp\u003e2.4.1 TheSampling Theorem.\u003c\/p\u003e \u003cp\u003e2.4.2 Image Resampling.\u003c\/p\u003e \u003cp\u003e2.4.3 Log-polar Mapping.\u003c\/p\u003e \u003cp\u003e2.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Template Matching as Testing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Detectionand Estimation.\u003c\/p\u003e \u003cp\u003e3.2 Hypothesis Testing.\u003c\/p\u003e \u003cp\u003e3.2.1 The Bayes RiskCriterion.\u003c\/p\u003e \u003cp\u003e3.2.2 The Neyman–Pearson Criterion.\u003c\/p\u003e \u003cp\u003e3.3 An Important Example.\u003c\/p\u003e \u003cp\u003e3.4 A Signal Processing Perspective: Matched Filters.\u003c\/p\u003e \u003cp\u003e3.5 Pattern Variability and the Normalized Correlation Coefficient.\u003c\/p\u003e \u003cp\u003e3.6 Estimation.\u003c\/p\u003e \u003cp\u003e3.6.1 Maximum Likelihood Estimation.\u003c\/p\u003e \u003cp\u003e3.6.2 Bayes Estimation.\u003c\/p\u003e \u003cp\u003e3.6.3 James–Stein Estimation.\u003c\/p\u003e \u003cp\u003e3.7 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Robust Similarity Estimators.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Robustness Measures.\u003c\/p\u003e \u003cp\u003e4.2 M-estimators.\u003c\/p\u003e \u003cp\u003e4.3 \u003ci\u003eL\u003c\/i\u003e\u003csub\u003e1\u003c\/sub\u003e Similarity Measures.\u003c\/p\u003e \u003cp\u003e4.4 Robust Estimation of Covariance Matrices.\u003c\/p\u003e \u003cp\u003e4.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Ordinal Matching Measures.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Ordinal Correlation Measures.\u003c\/p\u003e \u003cp\u003e5.1.1 Spearman Rank Correlation.\u003c\/p\u003e \u003cp\u003e5.1.2 Kendall Correlation.\u003c\/p\u003e \u003cp\u003e5.1.3 Bhat–Nayar Correlation.\u003c\/p\u003e \u003cp\u003e5.2 Non-parametric Local Transforms.\u003c\/p\u003e \u003cp\u003e5.2.1 The Census and Rank Transforms.\u003c\/p\u003e \u003cp\u003e5.2.2 Incremental Sign Correlation.\u003c\/p\u003e \u003cp\u003e5.3 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Matching Variable Patterns.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Multiclass Synthetic Discriminant Functions.\u003c\/p\u003e \u003cp\u003e6.2 Advanced Synthetic Discriminant Functions.\u003c\/p\u003e \u003cp\u003e6.3 Non-orthogonal Image Expansion.\u003c\/p\u003e \u003cp\u003e6.4 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Matching Linear Structure: The Hough Transform.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Getting Shapes: Edge Detection.\u003c\/p\u003e \u003cp\u003e7.2 The Radon Transform.\u003c\/p\u003e \u003cp\u003e7.3 The Hough Transform: Line and Circle Detection.\u003c\/p\u003e \u003cp\u003e7.4 The Generalized Hough Transform.\u003c\/p\u003e \u003cp\u003e7.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Low-dimensionality Representations and Matching.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Principal Components.\u003c\/p\u003e \u003cp\u003e8.1.1 Probabilistic PCA.\u003c\/p\u003e \u003cp\u003e8.1.2 How Many Components?\u003c\/p\u003e \u003cp\u003e8.2 ANonlinear Approach: Kernel PCA.\u003c\/p\u003e \u003cp\u003e8.3 Independent Components.\u003c\/p\u003e \u003cp\u003e8.4 Linear Discriminant Analysis.\u003c\/p\u003e \u003cp\u003e8.4.1 Bayesian Dual Spaces.\u003c\/p\u003e \u003cp\u003e8.5 A Sample Application: Photographic-quality Facial Composites.\u003c\/p\u003e \u003cp\u003e8.6 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Deformable Templates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 A Dynamic Perspective on the Hough Transform.\u003c\/p\u003e \u003cp\u003e9.2 Deformable Templates.\u003c\/p\u003e \u003cp\u003e9.3 Active Shape Models.\u003c\/p\u003e \u003cp\u003e9.4 DiffeomorphicMatching.\u003c\/p\u003e \u003cp\u003e9.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Computational Aspects of Template Matching.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Speed.\u003c\/p\u003e \u003cp\u003e10.1.1 Early Jump-out.\u003c\/p\u003e \u003cp\u003e10.1.2 TheUse of SumTables.\u003c\/p\u003e \u003cp\u003e10.1.3 Hierarchical Template Matching.\u003c\/p\u003e \u003cp\u003e10.1.4 Metric Inequalities.\u003c\/p\u003e \u003cp\u003e10.1.5 The FFT Advantage.\u003c\/p\u003e \u003cp\u003e10.1.6 PCA-basedSpeed-up.\u003c\/p\u003e \u003cp\u003e10.1.7 A Combined Approach.\u003c\/p\u003e \u003cp\u003e10.2 Precision.\u003c\/p\u003e \u003cp\u003e10.2.1 A Perturbative Approach.\u003c\/p\u003e \u003cp\u003e10.2.2 Phase Correlation.\u003c\/p\u003e \u003cp\u003e10.3 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Matching Point Sets: The Hausdorff Distance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Metric Pattern Spaces.\u003c\/p\u003e \u003cp\u003e11.2 Hausdorff Matching.\u003c\/p\u003e \u003cp\u003e11.3 Efficient Computation of the Hausdorff Distance.\u003c\/p\u003e \u003cp\u003e11.4 Partial Hausdorff Matching.\u003c\/p\u003e \u003cp\u003e11.5 Robustness Aspects.\u003c\/p\u003e \u003cp\u003e11.6 A Probabilistic Perspective.\u003c\/p\u003e \u003cp\u003e11.7 Invariant Moments.\u003c\/p\u003e \u003cp\u003e11.8 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Support Vector Machines and Regularization Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Learning and Regularization.\u003c\/p\u003e \u003cp\u003e12.2 RBF Networks.\u003c\/p\u003e \u003cp\u003e12.2.1 RBF Networks for Gender Recognition.\u003c\/p\u003e \u003cp\u003e12.3 Support Vector Machines.\u003c\/p\u003e \u003cp\u003e12.3.1 Improving Efficiency.\u003c\/p\u003e \u003cp\u003e12.3.2 Multiclass SVMs.\u003c\/p\u003e \u003cp\u003e12.3.3 Best Practice.\u003c\/p\u003e \u003cp\u003e12.4 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Feature Templates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Detecting Templates by Features.\u003c\/p\u003e \u003cp\u003e13.2 Parametric FeatureManifolds.\u003c\/p\u003e \u003cp\u003e13.3 Multiclass Pattern Rejection.\u003c\/p\u003e \u003cp\u003e13.4 Template Features.\u003c\/p\u003e \u003cp\u003e13.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Building a Multibiometric System.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Systems.\u003c\/p\u003e \u003cp\u003e14.2 The Electronic Librarian.\u003c\/p\u003e \u003cp\u003e14.3 Score Integration.\u003c\/p\u003e \u003cp\u003e14.4 Rejection.\u003c\/p\u003e \u003cp\u003e14.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendices.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA AnImAl: A Software Environment for Fast Prototyping.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 AnImAl: An Image Algebra.\u003c\/p\u003e \u003cp\u003eA.2 Image Representationand Processing Abstractions.\u003c\/p\u003e \u003cp\u003eA.3 The AnImAl Environment.\u003c\/p\u003e \u003cp\u003eA.4 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB Synthetic Oracles for Algorithm Development.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Computer Graphics.\u003c\/p\u003e \u003cp\u003eB.2 Describing Reality: Flexible Rendering Languages.\u003c\/p\u003e \u003cp\u003eB.3 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC On Evaluation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 A Note on Performance Evaluation.\u003c\/p\u003e \u003cp\u003eC.2 Traininga Classifier.\u003c\/p\u003e \u003cp\u003eC.3 Analyzing the Performance of a Classifier.\u003c\/p\u003e \u003cp\u003eC.4 Evaluating a Technology.\u003c\/p\u003e \u003cp\u003eC.5 Bibliographical Remarks.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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