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E-raamat: Robust Computer Vision: Theory and Applications

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Robust Computer Vision: Theory and ApplicationsSuurem pilt
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Computer image recognition systems must estimate accurate model parameters despite small-scale noise in the data and occasional large- scale measurement errors. This monograph presents a framework for a maximum likelihood approach to solving parameter estimation problems in computer vision applications involving similarity, then applies the framework to experiments on color based retrieval of images and objects, robust texture analysis, shape based retrieval, stereo matching, motion tracking, and facial expression recognition. The authors (Leiden University) survey literature on robust techniques in each particular field, discuss the results of comparative experiments, and conclude each chapter with observations. Annotation (c) Book News, Inc., Portland, OR (booknews.com)

From the foreword by Thomas Huang: "During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented.Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision."
Foreword xi
Preface xiii
1. INTRODUCTION 1(24)
1 Visual Similarity
2(14)
1.1 Color
4(3)
1.2 Texture
7(2)
1.3 Shape
9(2)
1.4 Stereo
11(2)
1.5 Motion
13(1)
1.6 Facial expression
13(2)
1.7 Summary
15(1)
2 Evaluation of Computer Vision Algorithms
16(3)
3 Overview of the Book
19(6)
2. MAXIMUM LIKELIHOOD FRAMEWORK 25 (36)
1 Introduction
25(1)
2 Statistical Distributions
26(17)
2.1 Gaussian Distribution
27(11)
2.2 Exponential Distribution
38(3)
2.3 Cauchy Distribution
41(2)
3 Robust Statistics
43(2)
3.1 Outliers
44(1)
4 Maximum Likelihood Estimators
45(2)
5 Maximum Likelihood in Relation to Other Approaches
47(3)
6 Our Maximum Likelihood Approach
50(7)
6.1 Scale Parameter Estimation in a Cauchy Distribution
54(3)
7 Experimental Setup
57(2)
8 Concluding Remarks
59(2)
3. COLOR BASED RETRIEVAL 61(22)
1 Introduction
61(3)
2 Colorimetry
64(1)
3 Color Models
64(4)
3.1 RGB Color system
65(1)
3.2 HSV Color System
66 (1)
3.3 l1 l2 l3 Color System
67(1)
4 Color Based Retrieval
68(5)
4.1 Color indexing
69(4)
5 Experiments with the Corel Database
73 (6)
5.1 Early Experiments
73 (1)
5.2 Usability Issues
74 (1)
5.3 Printer-Scanner Noise Experiments
75 (1)
5.4 Color Model
76 (1)
5.5 Quantization
76 (1)
5.6 Distribution Analysis
77(2)
6 Experiments with the Objects Database
79(2)
7 Concluding Remarks
81(2)
4. ROBUST TEXTURE ANALYSIS 83(28)
1 Introduction
83(3)
2 Human Perception of Texture
86(1)
3 Texture Features
87 (8)
3.1 Texture Distribution Models
88 (4)
3.1.1 Gray-level differences
89 (1)
3.1.2 Laws' texture energy measures
89 (1)
3.1.3 Center-symmetric covariance measures
89 (2)
3.1.4 Local binary patterns and trigrams
91 (1)
3.1.5 Complementary feature pairs
91 (1)
3.2 Gabor and Wavelet Models
92(3)
4 Texture Classification Experiments
95 (9)
4.2 Distribution Analysis
97 (2)
4.3 Misdetection Rates
99 (5)
4.3. 1 Summary
104(1)
5 Texture Retrieval Experiments
104(5)
5.1 Texture Features
105(1)
5.2 Experiments Setup
106(1)
5.3 Similarity Noise for QMF-Wavelet Transform
106(2)
5.4 Similarity Noise for Gabor Wavelet Transform
108(1)
6 Concluding Remarks
109(2)
5. SHAPE BASED RETRIEVAL 111 (24)
1 Introduction
111(2)
2 Human Perception of Visual Form
113(5)
3 Active Contours
118(12)
3.1 Behavior of Traditional Active Contours
120(4)
3.2 Generalized Force Balance Equations
124(1)
3.3 Gradient Vector Flow
125(5)
4 Invariant Moments
130(1)
5 Experiments
131(3)
6 Conclusions
134(1)
6. ROBUST STEREO MATCHING AND MOTION TRACKING 135(28)
1 Introduction
135(3)
1.1 Stereoscopic Vision
137(1)
2 Stereo Matching
138(6)
2.1 Related Work
142(2)
3 Stereo Matching Algorithms
144(6)
3.1 Template Based Algorithm
144(2)
3.2 Multiple Windows Algorithm
146(1)
3.3 Cox' Maximum Likelihood Algorithm
147(3)
4 Stereo Matching Experiments
150(7)
4.1 Stereo sets
151(1)
4.2 Stereo Matching Results
151(6)
4.3 Summary
157(1)
5 Motion Tracking Experiments
157(3)
6 Concluding Remarks
160(3)
7. FACIAL EXPRESSION RECOGNITION 163 (36)
1 Introduction
164(2)
2 Emotion Recognition
166(5)
2.1 Judgment Studies
167(1)
2.2 Review of Facial Expression Recognition
167(4)
3 Face Tracking and Feature Extraction
171(2)
4 The static Approach: Bayesian Network Classifiers
173(6)
4.1 Continuous Naive-Bayes: Gaussian and Cauchy Naive Bayes Classifiers
175(1)
4.2 Beyond the Naive-Bayes Assumption: Finding Dependencies among Features Using a Gaussian TAN Classifier
176(3)
5 The Dynamic Approach: Expression Recognition Using Multi-level HMMs
179(8)
5.1 Hidden Markov Models
182(1)
5.2 Expression Recognition Using Emotion-Specific HMMs
183(1)
5.3 Automatic Segmentation and Recognition of Emotions Using Multi-level HMMs
184(3)
6 Experiments
187 (8)
6.1 Results Using the Chen Database
191 (3)
6.1.1 Person-Dependent Tests
191 (2)
6.1.2 Person-Independent Tests
193 (1)
6.2 Results Using the Cohn-Kanade Database
194(1)
7 Summary and Discussion
195(4)
References 199(11)
Index 210


Nicu Sebe received his PhD degree from Leiden University in 2001. Currently, he is an Assistant Professor at Leiden University in the Netherlands. His main interest is in the fields of computer vision and pattern recognition, in particular content-based retrieval and robust techniques in computer vision. He was co-editing the proceedings of the International Conference on Image and Video Retrieval 2002. He is also acting as the technical program co-chair for the International Conference on Image and Video Retrieval 2003.



Michael S. Lew received his PhD degree in Electrical Engineering from the University of Illinois at Urbana-Champaign. He is currently an Associate Professor at Leiden University in the Netherlands. He has published over 100 scientific papers and helped organize several large conferences including IEEE Multimedia, ACM Multimedia, and the International Conference on Image and Video Retrieval.
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