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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 perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to…mehr
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- Produktdetails
- Verlag: John Wiley & Sons
- Seitenzahl: 348
- Erscheinungstermin: 29. April 2009
- Englisch
- ISBN-13: 9780470744048
- Artikelnr.: 37298947
- Verlag: John Wiley & Sons
- Seitenzahl: 348
- Erscheinungstermin: 29. April 2009
- Englisch
- ISBN-13: 9780470744048
- Artikelnr.: 37298947
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 TheSampling Theorem. 2.4.2 Image
Resampling. 2.4.3 Log-polar Mapping. 2.5 Bibliographical Remarks.
References. 3 Template Matching as Testing. 3.1 Detectionand Estimation.
3.2 Hypothesis Testing. 3.2.1 The Bayes RiskCriterion. 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 ANonlinear 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 DiffeomorphicMatching. 9.5
Bibliographical Remarks. References. 10 Computational Aspects of Template
Matching. 10.1 Speed. 10.1.1 Early Jump-out. 10.1.2 TheUse of SumTables.
10.1.3 Hierarchical Template Matching. 10.1.4 Metric Inequalities. 10.1.5
The FFT Advantage. 10.1.6 PCA-basedSpeed-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 FeatureManifolds. 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
Representationand 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 Traininga Classifier.
C.3 Analyzing the Performance of a Classifier. C.4 Evaluating a Technology.
C.5 Bibliographical Remarks. References. Index.
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 TheSampling Theorem. 2.4.2 Image
Resampling. 2.4.3 Log-polar Mapping. 2.5 Bibliographical Remarks.
References. 3 Template Matching as Testing. 3.1 Detectionand Estimation.
3.2 Hypothesis Testing. 3.2.1 The Bayes RiskCriterion. 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 ANonlinear 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 DiffeomorphicMatching. 9.5
Bibliographical Remarks. References. 10 Computational Aspects of Template
Matching. 10.1 Speed. 10.1.1 Early Jump-out. 10.1.2 TheUse of SumTables.
10.1.3 Hierarchical Template Matching. 10.1.4 Metric Inequalities. 10.1.5
The FFT Advantage. 10.1.6 PCA-basedSpeed-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 FeatureManifolds. 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
Representationand 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 Traininga Classifier.
C.3 Analyzing the Performance of a Classifier. C.4 Evaluating a Technology.
C.5 Bibliographical Remarks. References. Index.