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• 1. Vision, the Challenge
• 2. Images and Imaging Operations
• 3. Basic Image Filtering Operations
• 4. Thresholding Techniques
• 5. Edge Detection
• 6. Corner and Interest Point Detection
• 7. Mathematical Morphology
• 8. Texture
• 9. Binary Shape Analysis
• 10. Boundary Pattern Analysis
• 11. Line Detection
• 12. Circle and Ellipse Detection
• 13. The Hough Transform and Its Nature
• 14. Abstract Pattern Matching Techniques
• 15. The Three-Dimensional World
• 16. Tackling the perspective n-point problem
• 17. Invariants and perspective
• 18. Image transformations and camera calibration
• 19. Motion
• 20. Automated Visual Inspection
• 21. Inspection of Cereal Grains
• 22. Surveillance
• 23. In-Vehicle Vision Systems24 Statistical Pattern Recognition
• 25. Image Acquisition
• 26. Real-Time Hardware and Systems Design Considerations
• 27. Epilogue - Perspectives in Vision Appendix Robust statistics.
• (source: Nielsen Book Data)

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the `ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The `recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Machine learning and statistics overview
• 3. Performance measures I
• 4. Performance measures II
• 5. Error estimation
• 6. Statistical significance testing
• 7. Data sets and experimental framework
• 8. Recent developments
• 9. Conclusion
• Appendix A: statistical tables
• Appendix B: additional information on the data
• Appendix C: two case studies.
• (source: Nielsen Book Data)

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
(source: Nielsen Book Data)

• Preface xv
• 1 Introduction 1 1.1 From Fundamental to Applied 2 1.2 Part I: Color Fundamentals 3 1.3 Part II: Photometric Invariance 3 1.4 Part III: Color Constancy 4 1.5 Part IV: Color Feature Extraction 5 1.6 Part V: Applications 7 1.7 Summary 9 PART I Color Fundamentals 11
• 2 Color Vision 13 2.1 Introduction 13 2.2 Stages of Color Information Processing 14 2.3 Chromatic Properties of the Visual System 18 2.4 Summary 24
• 3 Color Image Formation 26 3.1 Lambertian Reflection Model 28 3.2 Dichromatic Reflection Model 29 3.3 Kubelka--Munk Model 32 3.4 The Diagonal Model 34 3.5 Color Spaces 36 3.6 Summary 44 PART II Photometric Invariance 47
• 4 Pixel-Based Photometric Invariance 49 4.1 Normalized Color Spaces 50 4.2 Opponent Color Spaces 52 4.3 The HSV Color Space 52 4.4 Composed Color Spaces 53 4.5 Noise Stability and Histogram Construction 58 4.6 Application: Color-Based Object Recognition 64 4.7 Summary 68
• 5 Photometric Invariance from Color Ratios 69 5.1 Illuminant Invariant Color Ratios 71 5.2 Illuminant Invariant Edge Detection 73 5.3 Blur-Robust and Color Constant Image Description 74 5.4 Application: Image Retrieval Based on Color Ratios 77 5.5 Summary 80
• 6 Derivative-Based Photometric Invariance 81 6.1 Full Photometric Invariants 84 6.2 Quasi-Invariants 101 6.3 Summary 111
• 7 Photometric Invariance by Machine Learning 113 7.1 Learning from Diversified Ensembles 114 7.2 Temporal Ensemble Learning 119 7.3 Learning Color Invariants for Region Detection 120 7.4 Experiments 124 7.5 Summary 134 PART III Color Constancy 135
• 8 Illuminant Estimation and Chromatic Adaptation 137 8.1 Illuminant Estimation 139 8.2 Chromatic Adaptation 141
• 9 Color Constancy Using Low-level Features 143 9.1 General Gray-World 143 9.2 Gray-Edge 146 9.3 Physics-Based Methods 150 9.4 Summary 151
• 10 Color Constancy Using Gamut-Based Methods 152 10.1 Gamut Mapping Using Derivative Structures 155 10.2 Combination of Gamut Mapping Algorithms 157 10.3 Summary 160
• 11 Color Constancy Using Machine Learning 161 11.1 Probabilistic Approaches 161 11.2 Combination Using Output Statistics 162 11.3 Combination Using Natural Image Statistics 163 11.4 Methods Using Semantic Information 167 11.5 Summary 171
• 12 Evaluation of Color Constancy Methods 172 12.1 Data Sets 172 12.2 Performance Measures 175 12.3 Experiments 180 12.4 Summary 185 PART IV Color Feature Extraction 187
• 13 Color Feature Detection 189 13.1 The Color Tensor 191 13.2 Color Saliency 205 13.3 Conclusions 218
• 14 Color Feature Description 221 14.1 Gaussian Derivative-Based Descriptors 225 14.2 Discriminative Power 229 14.3 Level of Invariance 235 14.4 Information Content 236 14.5 Summary 243
• 15 Color Image Segmentation 244 15.1 Color Gabor Filtering 245 15.2 Invariant Gabor Filters Under Lambertian Reflection 247 15.3 Color-Based Texture Segmentation 247 15.4 Material Recognition Using Invariant Anisotropic Filtering 249 15.5 Color Invariant Codebooks and Material-Specific Adaptation 256 15.6 Experiments 258 15.7 Image Segmentation by Delaunay Triangulation 263 15.8 Summary 268 PART V Applications 269
• 16 Object and Scene Recognition 271 16.1 Diagonal Model 272 16.2 Color SIFT Descriptors 273 16.3 Object and Scene Recognition 276 16.4 Results 280 16.5 Summary 285
• 17 Color Naming 287 17.1 Basic Color Terms 288 17.3 Color Names from Uncalibrated Data 304 17.4 Experimental Results 313 17.5 Conclusions 316
• 18 Segmentation of Multispectral Images 318 18.1 Reflection and Camera Models 319 18.2 Photometric Invariant Distance Measures 321 18.3 Error Propagation 325 18.4 Photometric Invariant Region Detection by Clustering 328 18.5 Experiments 330 18.6 Summary 338 Citation Guidelines 339 References 341 Index 363.
• (source: Nielsen Book Data)

Color is an important aspect of vision. However, most computer vision applications ignore color in their methods. This book addresses the use and application of color in the field of computer vision. First, the basics of color vision and the models that are used throughout the book are described. Then, photometric invariance, i.e. invariance to distruptiveimaging factors like shadow/shading and altering color of the light source, is discussed. After that, the detection and description of color image features is explained, and the book ends with some applications in which the use of color in computer vision is shown.
(source: Nielsen Book Data)
While the field of computer vision drives many of today's digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding.Based on the authors' intense collaboration for more than a decade and drawing on the latest thinking in the field of computer science, the book integrates topics from color science and computer vision, clearly linking theories, techniques, machine learning, and applications. The fundamental basics, sample applications, and downloadable versions of the software and data sets are also included. Clear, thorough, and practical, "Color in ""Computer Vision "explains: Computer vision, including color-driven algorithms and quantitative results of various state-of-the-art methodsColor science topics such as color systems, color reflection mechanisms, color invariance, and color constancyDigital image processing, including edge detection, feature extraction, image segmentation, and image transformationsSignal processing techniques for the development of both image processing and machine learningRobotics and artificial intelligence, including such topics as supervised learning and classifiers for object and scene categorization Researchers and professionals in computer science, computer vision, color science, electrical engineering, and signal processing will learn how to implement color in computer vision applications and gain insight into future developments in this dynamic and expanding field.
(source: Nielsen Book Data)

• Preface ix
• Numbers 1
• A 7
• B 25
• C 40
• D 71
• E 86
• F 94
• G 106
• H 119
• I 127
• J 143
• K 144
• L 148
• M 162
• N 185
• O 192
• P 201
• Q 225
• R 228
• S 245
• T 286
• U 299
• V 303
• W 314
• X 320
• Y 321
• Z 322
• References 324.
• (source: Nielsen Book Data)

Written by leading researchers, the 2nd Edition of the Dictionary of Computer Vision & Image Processing is a comprehensive and reliable resource which now provides explanations of over 3500 of the most commonly used terms across image processing, computer vision and related fields including machine vision. It offers clear and concise definitions with short examples or mathematical precision where necessary for clarity that ultimately makes it a very usable reference for new entrants to these fields at senior undergraduate and graduate level, through to early career researchers to help build up knowledge of key concepts. As the book is a useful source for recent terminology and concepts, experienced professionals will also find it a valuable resource for keeping up to date with the latest advances. New features of the 2nd Edition: Contains more than 1000 new terms, notably an increased focus on image processing and machine vision terms; Includes the addition of reference links across the majority of terms pointing readers to further information about the concept under discussion so that they can continue to expand their understanding; Now available as an eBook with enhanced content: approximately 50 videos to further illustrate specific terms; active cross-linking between terms so that readers can easily navigate from one related term to another and build up a full picture of the topic in question; and hyperlinked references to fully embed the text in the current literature.
(source: Nielsen Book Data)

• 1. Scaling up machine learning: introduction Ron Bekkerman, Mikhail Bilenko and John Langford
• Part I. Frameworks for Scaling Up Machine Learning: 2. Mapreduce and its application to massively parallel learning of decision tree ensembles Biswanath Panda, Joshua S. Herbach, Sugato Basu and Roberto J. Bayardo
• 3. Large-scale machine learning using DryadLINQ Mihai Budiu, Dennis Fetterly, Michael Isard, Frank McSherry and Yuan Yu
• 4. IBM parallel machine learning toolbox Edwin Pednault, Elad Yom-Tov and Amol Ghoting
• 5. Uniformly fine-grained data parallel computing for machine learning algorithms Meichun Hsu, Ren Wu and Bin Zhang
• Part II. Supervised and Unsupervised Learning Algorithms: 6. PSVM: parallel support vector machines with incomplete Cholesky Factorization Edward Chang, Hongjie Bai, Kaihua Zhu, Hao Wang, Jian Li and Zhihuan Qiu
• 7. Massive SVM parallelization using hardware accelerators Igor Durdanovic, Eric Cosatto, Hans Peter Graf, Srihari Cadambi, Venkata Jakkula, Srimat Chakradhar and Abhinandan Majumdar
• 8. Large-scale learning to rank using boosted decision trees Krysta M. Svore and Christopher J. C. Burges
• 9. The transform regression algorithm Ramesh Natarajan and Edwin Pednault
• 10. Parallel belief propagation in factor graphs Joseph Gonzalez, Yucheng Low and Carlos Guestrin
• 11. Distributed Gibbs sampling for latent variable models Arthur Asuncion, Padhraic Smyth, Max Welling, David Newman, Ian Porteous and Scott Triglia
• 12. Large-scale spectral clustering with Mapreduce and MPI Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen Lin and Edward Y. Chang
• 13. Parallelizing information-theoretic clustering methods Ron Bekkerman and Martin Scholz
• Part III. Alternative Learning Settings: 14. Parallel online learning Daniel Hsu, Nikos Karampatziakis, John Langford and Alex J. Smola
• 15. Parallel graph-based semi-supervised learning Jeff Bilmes and Amarnag Subramanya
• 16. Distributed transfer learning via cooperative matrix factorization Evan Xiang, Nathan Liu and Qiang Yang
• 17. Parallel large-scale feature selection Jeremy Kubica, Sameer Singh and Daria Sorokina
• Part IV. Applications: 18. Large-scale learning for vision with GPUS Adam Coates, Rajat Raina and Andrew Y. Ng
• 19. Large-scale FPGA-based convolutional networks Clement Farabet, Yann LeCun, Koray Kavukcuoglu, Berin Martini, Polina Akselrod, Selcuk Talay and Eugenio Culurciello
• 20. Mining tree structured data on multicore systems Shirish Tatikonda and Srinivasan Parthasarathy
• 21. Scalable parallelization of automatic speech recognition Jike Chong, Ekaterina Gonina, Kisun You and Kurt Keutzer.
• (source: Nielsen Book Data)

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms, and deep dives into several applications, make the book equally useful for researchers, students and practitioners.
(source: Nielsen Book Data)

• Preface xv Acknowledgements xxi 1 Fundamentals of Pattern Recognition 1 1.1 Basic Concepts: Class Feature Data Set 1 1.1.1 Classes and Class Labels 1 1.1.2 Features 2 1.1.3 Data Set 3 1.1.4 Generate Your Own Data 6 1.2 Classifier Discriminant Functions Classification Regions 9 1.3 Classification Error and Classification Accuracy 11 1.3.1 Where Does the Error Come From? Bias and Variance 11 1.3.2 Estimation of the Error 13 1.3.3 Confusion Matrices and Loss Matrices 14 1.3.4 Training and Testing Protocols 15 1.3.5 Overtraining and Peeking 17 1.4 Experimental Comparison of Classifiers 19 1.4.1 Two Trained Classifiers and a Fixed Testing Set 20 1.4.2 Two Classifier Models and a Single Data Set 22 1.4.3 Two Classifier Models and Multiple Data Sets 26 1.4.4 Multiple Classifier Models and Multiple Data Sets 27 1.5 Bayes Decision Theory 30 1.5.1 Probabilistic Framework 301.5.2 Discriminant Functions and Decision Boundaries 31 1.5.3 Bayes Error 33 1.6 Clustering and Feature Selection 35 1.6.1 Clustering 35 1.6.2 Feature Selection 37 1.7 Challenges of Real-Life Data 40
• Appendix 41 1.A.1 Data Generation 41 1.A.2 Comparison of Classifiers 42 1.A.2.1 MATLAB Functions for Comparing Classifiers 42 1.A.2.2 Critical Values for Wilcoxon and Sign Test 45 1.A.3 Feature Selection 47 2 Base Classifiers 49 2.1 Linear and Quadratic Classifiers 49 2.1.1 Linear Discriminant Classifier 49 2.1.2 Nearest Mean Classifier 52 2.1.3 Quadratic Discriminant Classifier 52 2.1.4 Stability of LDC and QDC 53 2.2 Decision Tree Classifiers 55 2.2.1 Basics and Terminology 55 2.2.2 Training of Decision Tree Classifiers 57 2.2.3 Selection of the Feature for a Node 58 2.2.4 Stopping Criterion 60 2.2.5 Pruning of the Decision Tree 63 2.2.6 C4.5 and ID3 64 2.2.7 Instability of Decision Trees 64 2.2.8 Random Trees 65 2.3 The Nayve Bayes Classifier 66 2.4 Neural Networks 68 2.4.1 Neurons 68 2.4.2 Rosenblatt s Perceptron 70 2.4.3 Multi-Layer Perceptron 71 2.5 Support Vector Machines 73 2.5.1 Why Would It Work? 73 2.5.2 Classification Margins 74 2.5.3 Optimal Linear Boundary 76 2.5.4 Parameters and Classification Boundaries of SVM 78 2.6 The k-Nearest Neighbor Classifier (k-nn) 80 2.7 Final Remarks 82 2.7.1 Simple or Complex Models? 82 2.7.2 The Triangle Diagram 83 2.7.3 Choosing a Base Classifier for Ensembles 85
• Appendix 85 2.A.1 MATLAB Code for the Fish Data 85 2.A.2 MATLAB Code for Individual Classifiers 86 2.A.2.1 Decision Tree 86 2.A.2.2 Nayve Bayes 89 2.A.2.3 Multi-Layer Perceptron 90 2.A.2.4 1-nn Classifier 92 3 An Overview of the Field 94 3.1 Philosophy 94 3.2 Two Examples 98 3.2.1 The Wisdom of the Classifier Crowd 98 3.2.2 The Power of Divide-and-Conquer 98 3.3 Structure of the Area 100 3.3.1 Terminology 100 3.3.2 A Taxonomy of Classifier Ensemble Methods 100 3.3.3 Classifier Fusion and Classifier Selection 104 3.4 Quo Vadis? 105 3.4.1 Reinventing the Wheel? 105 3.4.2 The Illusion of Progress? 106 3.4.3 A Bibliometric Snapshot 107 4 Combining Label Outputs 111 4.1 Types of Classifier Outputs 111 4.2 A Probabilistic Framework for Combining Label Outputs 112 4.3 Majority Vote 113 4.3.1 Democracy in Classifier Combination 113 4.3.2 Accuracy of the Majority Vote 114 4.3.3 Limits on the Majority Vote Accuracy: An Example 117 4.3.4 Patterns of Success and Failure 119 4.3.5 Optimality of the Majority Vote Combiner 124 4.4 Weighted Majority Vote 125 4.4.1 Two Examples 126 4.4.2 Optimality of the Weighted Majority Vote Combiner 127 4.5 Nayve-Bayes Combiner 128 4.5.1 Optimality of the Nayve Bayes Combiner 128 4.5.2 Implementation of the NB Combiner 130 4.6 Multinomial Methods 132 4.7 Comparison of Combination Methods for Label Outputs 135
• Appendix 137 4.A.1 Matan s Proof for the Limits on the Majority Vote Accuracy 137 4.A.2 Selected MATLAB Code 139 5 Combining Continuous-Valued Outputs 143 5.1 Decision Profile 143 5.2 How Do We Get Probability Outputs? 144 5.2.1 Probabilities Based on Discriminant Scores 144 5.2.2 Probabilities Based on Counts: Laplace Estimator 147 5.3 Nontrainable (Fixed) Combination Rules 150 5.3.1 A Generic Formulation 150 5.3.2 Equivalence of Simple Combination Rules 152 5.3.3 Generalized Mean Combiner 153 5.3.4 A Theoretical Comparison of Simple Combiners 156 5.3.5 Where Do They Come From? 160 5.4 The Weighted Average (Linear Combiner) 166 5.4.1 Consensus Theory 166 5.4.2 Added Error for the Weighted Mean Combination 167 5.4.3 Linear Regression 168 5.5 A Classifier as a Combiner 172 5.5.1 The Supra Bayesian Approach 172 5.5.2 Decision Templates 173 5.5.3 A Linear Classifier 175 5.6 An Example of Nine Combiners for Continuous-Valued Outputs 175 5.7 To Train or Not to Train? 176
• Appendix 178 5.A.1 Theoretical Classification Error for the Simple Combiners 178 5.A.1.1 Set-up and Assumptions 178 5.A.1.2 Individual Error 180 5.A.1.3 Minimum and Maximum 180 5.A.1.4 Average (Sum) 181 5.A.1.5 Median and Majority Vote 182 5.A.1.6 Oracle 183 5.A.2 Selected MATLAB Code 183 6 Ensemble Methods 186 6.1 Bagging 186 6.1.1 The Origins: Bagging Predictors 186 6.1.2 Why Does Bagging Work? 187 6.1.3 Out-of-bag Estimates 189 6.1.4 Variants of Bagging 190 6.2 Random Forests 190 6.3 AdaBoost 192 6.3.1 The AdaBoost Algorithm 192 6.3.2 The arc-x4 Algorithm 194 6.3.3 Why Does AdaBoost Work? 195 6.3.4 Variants of Boosting 199 6.3.5 A Famous Application: AdaBoost for Face Detection 199 6.4 Random Subspace Ensembles 203 6.5 Rotation Forest 204 6.6 Random Linear Oracle 208 6.7 Error Correcting Output Codes (ECOC) 211 6.7.1 Code Designs 212 6.7.2 Decoding 214 6.7.3 Ensembles of Nested Dichotomies 216
• Appendix 218 6.A.1 Bagging 218 6.A.2 AdaBoost 220 6.A.3 Random Subspace 223 6.A.4 Rotation Forest 225 6.A.5 Random Linear Oracle 228 6.A.6 ECOC 229 7 Classifier Selection 230 7.1 Preliminaries 230 7.2 Why Classifier Selection Works 231 7.3 Estimating Local Competence Dynamically 233 7.3.1 Decision-Independent Estimates 233 7.3.2 Decision-Dependent Estimates 238 7.4 Pre-Estimation of the Competence Regions 239 7.4.1 Bespoke Classifiers 240 7.4.2 Clustering and Selection 241 7.5 Simultaneous Training of Regions and Classifiers 242 7.6 Cascade Classifiers 244 Appendix: Selected MATLAB Code 244 7.A.1 Banana Data 244 7.A.2 Evolutionary Algorithm for a Selection Ensemble for the Banana Data 245 8 Diversity in Classifier Ensembles 247 8.1 What Is Diversity? 247 8.1.1 Diversity for a Point-Value Estimate 248 8.1.2 Diversity in Software Engineering 248 8.1.3 Statistical Measures of Relationship 249 8.2 Measuring Diversity in Classifier Ensembles 250 8.2.1 Pairwise Measures 250 8.2.2 Nonpairwise Measures 251 8.3 Relationship Between Diversity and Accuracy 256 8.3.1 An Example 256 8.3.2 Relationship Patterns 258 8.3.3 A Caveat: Independent Outputs Independent Errors 262 8.3.4 Independence Is Not the Best Scenario 265 8.3.5 Diversity and Ensemble Margins 267 8.4 Using Diversity 270 8.4.1 Diversity for Finding Bounds and Theoretical Relationships 270 8.4.2 Kappa-error Diagrams and Ensemble Maps 271 8.4.3 Overproduce and Select 275 8.5 Conclusions: Diversity of Diversity 279
• Appendix 280 8.A.1 Derivation of Diversity Measures for Oracle Outputs 280 8.A.1.1 Correlation 280 8.A.1.2 Interrater Agreement 281 8.A.2 Diversity Measure Equivalence 282 8.A.3 Independent Outputs Independent Errors 284 8.A.4 A Bound on the Kappa-Error Diagram 286 8.A.5 Calculation of the Pareto Frontier 287 9 Ensemble Feature Selection 290 9.1 Preliminaries 290 9.1.1 Right and Wrong Protocols 290 9.1.2 Ensemble Feature Selection Approaches 294 9.1.3 Natural Grouping 294 9.2 Ranking by Decision Tree Ensembles 295 9.2.1 Simple Count and Split Criterion 295 9.2.2 Permuted Features or the Noised-up Method 297 9.3 Ensembles of Rankers 299 9.3.1 The Approach 299 9.3.2 Ranking Methods (Criteria) 300 9.4 Random Feature Selection for the Ensemble 305 9.4.1 Random Subspace Revisited 305 9.4.2 Usability Coverage and Feature Diversity 306 9.4.3 Genetic Algorithms 312 9.5 Nonrandom Selection 315 9.5.1 The Favorite Class Model 315 9.5.2 The Iterative Model 315 9.5.3 The Incremental Model 316 9.6 A Stability Index 317 9.6.1 Consistency Between a Pair of Subsets 317 9.6.2 A Stability Index for K Sequences 319 9.6.3 An Example of Applying the Stability Index 320
• Appendix 322 9.A.1 MATLAB Code for the Numerical Example of Ensemble Ranking 322 9.A.2 MATLAB GA Nuggets 322 9.A.3 MATLAB Code for the Stability Index 324 10 A Final Thought 326 References 327 Index 353.
• (source: Nielsen Book Data)

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLAB(R) code and practice data sets throughout, Combining Pattern Classifiers includes: * Coverage of Bayes decision theory and experimental comparison of classifiers * Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others * Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.
(source: Nielsen Book Data)

• Preface xix
• Acknowledgments xxvii
• Contributors xxix
• 1 Introduction to Emotion Recognition 1 Amit Konar, Anisha Halder, and Aruna Chakraborty
• 1.1 Basics of Pattern Recognition, 1
• 1.2 Emotion Detection as a Pattern Recognition Problem, 2
• 1.3 Feature Extraction, 3
• 1.4 Feature Reduction Techniques, 15
• 1.5 Emotion Classification, 17
• 1.6 Multimodal Emotion Recognition, 24
• 1.7 Stimulus Generation for Emotion Arousal, 24
• 1.8 Validation Techniques, 26
• 1.9 Summary, 27
• References, 28
• Author Biographies, 44
• 2 Exploiting Dynamic Dependencies Among Action Units for Spontaneous Facial Action Recognition 47 Yan Tong and Qiang Ji
• 2.1 Introduction, 48
• 2.2 Related Work, 49
• 2.3 Modeling the Semantic and Dynamic Relationships Among AUs With a DBN, 50
• 2.4 Experimental Results, 60
• 2.5 Conclusion, 64
• References, 64
• Author Biographies, 66
• 3 Facial Expressions: A Cross-Cultural Study 69 Chandrani Saha, Washef Ahmed, Soma Mitra, Debasis Mazumdar, and Sushmita Mitra
• 3.1 Introduction, 69
• 3.2 Extraction of Facial Regions and Ekman s Action Units, 71
• 3.3 Cultural Variation in Occurrence of Different AUs, 76
• 3.4 Classification Performance Considering Cultural Variability, 79
• 3.5 Conclusion, 84
• References, 84
• Author Biographies, 86
• 4 A Subject-Dependent Facial Expression Recognition System 89 Chuan-Yu Chang and Yan-Chiang Huang
• 4.1 Introduction, 89
• 4.2 Proposed Method, 91
• 4.3 Experiment Result, 103
• 4.4 Conclusion, 109
• Acknowledgment, 110
• References, 110
• Author Biographies, 112
• 5 Facial Expression Recognition Using Independent Component Features and Hidden Markov Model 113 Md. Zia Uddin and Tae-Seong Kim
• 5.1 Introduction, 114
• 5.2 Methodology, 115
• 5.3 Experimental Results, 123
• 5.4 Conclusion, 125
• Acknowledgments, 125
• References, 126
• Author Biographies, 127
• 6 Feature Selection for Facial Expression Based on Rough Set Theory 129 Yong Yang and Guoyin Wang
• 6.1 Introduction, 129
• 6.2 Feature Selection for Emotion Recognition Based on Rough Set Theory, 131
• 6.3 Experiment Results and Discussion, 137
• 6.4 Conclusion, 143
• Acknowledgments, 143
• References, 143
• Author Biographies, 145
• 7 Emotion Recognition from Facial Expressions Using Type-2 Fuzzy Sets 147 Anisha Halder, Amit Konar, Aruna Chakraborty, and Atulya K. Nagar
• 7.1 Introduction, 148
• 7.2 Preliminaries on Type-2 Fuzzy Sets, 150
• 7.3 Uncertainty Management in Fuzzy-Space for Emotion Recognition, 152
• 7.4 Fuzzy Type-2 Membership Evaluation, 157
• 7.5 Experimental Details, 161
• 7.6 Performance Analysis, 167
• 7.7 Conclusion, 175
• References, 176
• Author Biographies, 180
• 8 Emotion Recognition from Non-frontal Facial Images 183 Wenming Zheng, Hao Tang, and Thomas S. Huang
• 8.1 Introduction, 184
• 8.2 A Brief Review of Automatic Emotional Expression Recognition, 187
• 8.3 Databases for Non-frontal Facial Emotion Recognition, 191
• 8.4 Recent Advances of Emotion Recognition from Non-Frontal Facial Images, 196
• 8.5 Discussions and Conclusions, 205
• Acknowledgments, 206
• References, 206
• Author Biographies, 211
• 9 Maximum a Posteriori Based Fusion Method for Speech Emotion Recognition 215 Ling Cen, Zhu Liang Yu, and Wee Ser
• 9.1 Introduction, 216
• 9.2 Acoustic Feature Extraction for Emotion Recognition, 219
• 9.3 Proposed Map-Based Fusion Method, 223
• 9.4 Experiment, 229
• 9.5 Conclusion, 232
• References, 232
• Author Biographies, 234
• 10 Emotion Recognition in Naturalistic Speech and Language A Survey 237 Felix Weninger, Martin Wollmer, and Bjoern Schuller
• 10.1 Introduction, 238
• 10.2 Tasks and Applications, 239
• 10.3 Implementation and Evaluation, 244
• 10.4 Challenges, 253
• 10.5 Conclusion and Outlook, 257
• Acknowledgment, 259
• References, 259
• Author Biographies, 267
• 11 EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques 269 Panagiotis C. Petrantonakis and Leontios J. Hadjileontiadis
• 11.1 Introduction, 270
• 11.2 Brain Activity and Emotions, 271
• 11.3 EEG-ER Systems: An Overview, 272
• 11.4 Emotion Elicitation, 273
• 11.5 Advanced Signal Processing in EEG-ER, 275
• 11.6 Concluding Remarks and Future Directions, 287
• References, 289
• Author Biographies, 292
• 12 Frequency Band Localization on Multiple Physiological Signals for Human Emotion Classification Using DWT 295 M. Murugappan
• 12.1 Introduction, 296
• 12.2 Related Work, 297
• 12.3 Research Methodology, 299
• 12.4 Experimental Results and Discussions, 306
• 12.5 Conclusion, 310
• 12.6 Future Work, 310
• Acknowledgments, 310
• References, 310
• Author Biography, 312
• 13 Toward Affective Brain Computer Interface: Fundamentals and Analysis of EEG-Based Emotion Classification 315 Yuan-Pin Lin, Tzyy-Ping Jung, Yijun Wang, and Julie Onton
• 13.1 Introduction, 316
• 13.2 Materials and Methods, 323
• 13.3 Results and Discussion, 327
• 13.4 Conclusion, 332
• 13.5 Issues and Challenges Toward ABCIs, 332
• Acknowledgments, 336
• References, 336
• Author Biographies, 340
• 14 Bodily Expression for Automatic Affect Recognition 343 Hatice Gunes, Caifeng Shan, Shizhi Chen, and YingLi Tian
• 14.1 Introduction, 344
• 14.2 Background and Related Work, 345
• 14.3 Creating a Database of Facial and Bodily Expressions: The FABO Database, 353
• 14.4 Automatic Recognition of Affect from Bodily Expressions, 356
• 14.5 Automatic Recognition of Bodily Expression Temporal Dynamics, 361
• 14.6 Discussion and Outlook, 367
• 14.7 Conclusions, 369
• Acknowledgments, 370
• References, 370
• Author Biographies, 375
• 15 Building a Robust System for Multimodal Emotion Recognition 379 Johannes Wagner, Florian Lingenfelser, and Elisabeth Andre
• 15.1 Introduction, 380
• 15.2 Related Work, 381
• 15.3 The Callas Expressivity Corpus, 382
• 15.4 Methodology, 386
• 15.5 Multisensor Data Fusion, 390
• 15.6 Experiments, 395
• 15.7 Online Recognition System, 399
• 15.8 Conclusion, 403
• Acknowledgment, 404
• References, 404
• Author Biographies, 410
• 16 Semantic Audiovisual Data Fusion for Automatic Emotion Recognition 411 Dragos Datcu and Leon J. M. Rothkrantz
• 16.1 Introduction, 412
• 16.2 Related Work, 413
• 16.3 Data Set Preparation, 416
• 16.4 Architecture, 418
• 16.5 Results, 431
• 16.6 Conclusion, 432
• References, 432
• Author Biographies, 434
• 17 A Multilevel Fusion Approach for Audiovisual Emotion Recognition 437 Girija Chetty, Michael Wagner, and Roland Goecke
• 17.1 Introduction, 437
• 17.2 Motivation and Background, 438
• 17.3 Facial Expression Quantification, 440
• 17.4 Experiment Design, 444
• 17.5 Experimental Results and Discussion, 450
• 17.6 Conclusion, 456
• References, 456
• Author Biographies, 459
• 18 From a Discrete Perspective of Emotions to Continuous, Dynamic, and Multimodal Affect Sensing 461 Isabelle Hupont, Sergio Ballano, Eva Cerezo, and Sandra Baldassarri
• 18.1 Introduction, 462
• 18.2 A Novel Method for Discrete Emotional Classification of Facial Images, 465
• 18.3 A 2D Emotional Space for Continuous and Dynamic Facial Affect Sensing, 469
• 18.4 Expansion to Multimodal Affect Sensing, 474
• 18.5 Building Tools That Care, 479
• 18.6 Concluding Remarks and Future Work, 486
• Acknowledgments, 488
• References, 488
• Author Biographies, 491
• 19 Audiovisual Emotion Recognition Using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy 493 Chung-Hsien Wu, Jen-Chun Lin, and Wen-Li Wei
• 19.1 Introduction, 494
• 19.2 Feature Extraction, 495
• 19.3 Semi-Coupled Hidden Markov Model, 500
• 19.4 Experiments, 504
• 19.5 Conclusion, 508
• References, 509
• Author Biographies, 512
• 20 Emotion Recognition in Car Industry 515 Christos D. Katsis, George Rigas, Yorgos Goletsis, and Dimitrios I. Fotiadis
• 20.1 Introduction, 516
• 20.2 An Overview of Application for the Car Industry, 517
• 20.3 Modality-Based Categorization, 517
• 20.4 Emotion-Based Categorization, 520
• 20.5 Two Exemplar Cases, 523
• 20.6 Open Issues and Future Steps, 536
• 20.7 Conclusion, 537
• References, 537
• Author Biographies, 543
• Index 545.
• (source: Nielsen Book Data)

A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book Offers both foundations and advances on emotion recognition in a single volume Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains Inspires young researchers to prepare themselves for their own research Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
(source: Nielsen Book Data)

• 1. An Artificial Intelligence System Based Power Estimation Method for CMOS VLSI Circuits
• [Govindaraj Vellingiri and Ramesh Jayabalan]
• 2. Awareness Alert and Information Analysis in Social Media Networking Using Usage Analysis and Negotiable Approach
• [Alamelu Muthukrishnan]
• 3. Object Detection and Tracking in Video Using Deep Learning Techniques: A Review
• [Dhivya Praba Ramasamy and Kavitha Kanagaraj]
• 4. Fuzzy MCDM: Application in Disease Risk and Prediction
• [Rachna Jain, Meenu Gupta, Abhishek Kathuria, and Devanshi Mukhopadhyay]
• 5. Deep Learning Approach to Predict and Grade Glaucoma from Fundus Images through Constitutional Neural Networks
• [Kishore Balasubramanian and N. B. Ananthamoorthy]
• 6. A Novel Method for Securing Cognitive Radio Communication Network Using the Machine Learning Schemes and a Rule Based Approaches
• [Antony Hyils Sharon Magdalene and Lakshmanan Thulasimani]
• 7. Detection of Retinopathy of Prematurity Using Convolution Neural Network
• [Deepa Dhanaskodi and Poongodi Chenniappan]
• 8. Impact of Technology on Human Resource Information System and Achieving Business Intelligence in Organizations
• [Sharanika Dhal, Manas Kumar Pal, Archana Choudhary, and Mamata Rath]
• 9. Proficient Prediction of Acute Lymphoblastic Leukemia Using Machine Learning Algorithm
• [M. Sangeetha, K.N. Apinaya Prethi, and S. Nithya]
• 10. Role of Machine Learning in Social Area Networks
• [Rajeswari Arumugam, Premalatha Balasubramaniam, and Cynthia Joseph]
• 11. Breast Cancer and Machine Learning: Interactive Breast Cancer Prediction Using Naive Bayes Algorithm
• [Atapaka Thrilok Gayathri and Samuel Theodore Deepa]
• 12. Deep Networks and Deep Learning Algorithms
• [Tannu Kumari and Anjana Mishra]
• 13. Machine Learning for Big Data Analytics, Interactive and Reinforcement
• [Ritwik Raj and Anjana Mishra]
• 14. Fish Farm Monitoring System Using IoT and Machine Learning
• [Farjana Yeasmin Trisha, Mohammad Farhan Ferdous, and Mahmudul Hasan].
• (source: Nielsen Book Data)

Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning.
(source: Nielsen Book Data)

• Reader's Guide. Recognition and Learning By a Computer. Representing Information. Generation and Transformation of Representations. Pattern Feature Extraction. Pattern Feature Extraction. Pattern Understanding Methods. Learning Concepts. Learning Procedures. Learning Based on Logic. Learning Procedures. Learning Based on Logic. Learning By Classification and Discovery. Learning By Neural Network. Appendix. Answers to Exercises. Chapter Summaries, Keywords, And Exercises. Chapter References. Index.
• (source: Nielsen Book Data)

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.
(source: Nielsen Book Data)

• 1. Vision, the Challenge
• 2. Images and Imaging Operations
• 3. Image Filtering and Morphology
• 4. The Role of Thresholding
• 5. Edge Detection
• 6. Corner, Interest Point and Invariant Feature Detection
• 7. Texture Analysis
• 8. Binary Shape Analysis
• 9. Boundary Pattern Analysis
• 10. Line, Circle and Ellipse Detection
• 11. The Generalised Hough Transform
• 12. Object Segmentation and Shape Models
• 13. Basic Classification Concepts
• 14. Machine Learning: Probabilistic Methods
• 15. Deep Learning Networks
• 16. The Three-Dimensional World
• 17. Tackling the Perspective n-point Problem
• 18. Invariants and perspective
• 19. Image transformations and camera calibration
• 20. Motion
• 21. Face Detection and Recognition: the Impact of Deep Learning
• 22. Surveillance
• 23. In-Vehicle Vision Systems
• 24. Epilogue-Perspectives in Vision Appendix A: Robust statistics Appendix B: The Sampling Theorem Appendix C: The representation of colour Appendix D: Sampling from distributions.
• (source: Nielsen Book Data)

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/.
(source: Nielsen Book Data)

• 1. Vision, the Challenge
• 2. Images and Imaging Operations
• 3. Image Filtering and Morphology
• 4. The Role of Thresholding
• 5. Edge Detection
• 6. Corner, Interest Point and Invariant Feature Detection
• 7. Texture Analysis
• 8. Binary Shape Analysis
• 9. Boundary Pattern Analysis
• 10. Line, Circle and Ellipse Detection
• 11. The Generalised Hough Transform
• 12. Object Segmentation and Shape Models
• 13. Basic Classification Concepts
• 14. Machine Learning: Probabilistic Methods
• 15. Deep Learning Networks
• 16. The Three-Dimensional World
• 17. Tackling the Perspective n-point Problem
• 18. Invariants and perspective
• 19. Image transformations and camera calibration
• 20. Motion
• 21. Face Detection and Recognition: the Impact of Deep Learning
• 22. Surveillance
• 23. In-Vehicle Vision Systems
• 24. Epilogue-Perspectives in Vision Appendix A: Robust statistics Appendix B: The Sampling Theorem Appendix C: The representation of colour Appendix D: Sampling from distributions.
• (source: Nielsen Book Data)

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/.
(source: Nielsen Book Data)

• Preface
• Some Applications of Computer Vision Systems in Micromechanics
• A Survey of Face Recognition by the Genetic Algorithm
• The Attentive Co-Pilot: Robust Driver Assistance Relying on Human-Like Signal Processing Principles
• Traffic Monitoring: A Practical Implementation of a Real-Time Open Air Computer Vision System
• Algebraic Topology for Computer Vision
• Least Squares Fitting of Quadratic Curves & Surfaces
• Color Invariants for Object Detection
• Spectral Imaging Versus Non-Spectral Imaging
• Ontology Based Image & Video Analysis
• 3D Shape Descriptors
• Computer Vision by Laser Metrology & Algorithms of Artificial Intelligence
• Index.
• (source: Nielsen Book Data)

Computer vision is the science and technology of machines that see, where seeing in this case means that the machine is able to extract information from an image that is necessary to solve some task. This book presents topical research in the study of computer vision, including computer vision systems in micromechanics; genetic algorithm-based face recognition; algebraic topology for computer vision and computer vision by laser metrology and algorithms of artificial intelligence.
(source: Nielsen Book Data)

• Preface. Contributors. B. Jahne, Introduction. Sensors and Imaging. H. Haubecker, Radiation and Illumination. P. Geibler, Imaging Optics. H. Haubecker, Radiometry of Imaging. P. Seitz, Solid-State Image Sensing. R. Godding, Geometric Calibration of Digital Imaging Systems. R. Schwarte, G. Hausler, R.W. Malz, Three-Dimensional Imaging Techniques. Signal Processing and Pattern Recognition. B. Jahne, Representation of Multidimensional Signals. B. Jahne, Neighborhood Operators. H. Haubecker, H. Spies, Motion. P. Geibler, T. Dierig, H.A. Mallot, Three-Dimensional Imaging Algorithms. J. Weickert, Design of Nonlinear Diffusion Filters. C. Schnorr, Variational Adaptive Smoothing and Segmentation. P. Soille, Morphological Operators. J. Hornegger, D. Paulus, H. Niemann, Probabilistic Modeling in Computer Vision. H. Haubecker, H.R. Tizhoosh, Fuzzy Image Processing. A. Meyer-Base, Neural Net Computing for Image Processing. Application Gallery. Index.
• (source: Nielsen Book Data)

Based on the highly successful 3-volume reference Handbook of Computer Vision and Applications, this concise edition covers in a single volume the entire spectrum of computer vision ranging form the imaging process to high-end algorithms and applications. This book consists of three parts, including an application gallery.
(source: Nielsen Book Data)

• Chapter 1. Vision, the Challenge
• Part 1. Low-level Vision
• Chapter 2. Images and Imaging Operations
• Chapter 3. Basic Image Filtering Operations
• Chapter 4. Thresholding Techniques
• Chapter 5. Edge Detection
• Chapter 6. Corner and Interest Point Detection
• Chapter 7. Mathematical Morphology
• Chapter 8. Texture.

• Part 2. Intermediate-level Vision
• Chapter 9. Binary Shape Analysis
• Chapter 10. Boundary Pattern Analysis
• Chapter 11. Line Detection
• Chapter 12. Circle and Ellipse Detection
• Chapter 13. The Hough Transform and Its Nature
• Chapter 14. Pattern Matching Techniques.

• Part 3. 3-D Vision and Motion
• Chapter 15. The Three-Dimensional World
• Chapter 16. Tackling the Perspective n-point Problem
• Chapter 17. Invariants and Perspective
• Chapter 18. Image Transformations and Camera Calibration
• Chapter 19. Motion.

• Part 4. Toward Real-time Pattern Recognition Systems
• Chapter 20. Automated Visual Inspection
• Chapter 21. Inspection of Cereal Grains
• Chapter 22. Surveillance
• Chapter 23. In-Vehicle Vision Systems
• Chapter 24. Statistical Pattern Recognition
• Chapter 25. Image Acquisition
• Chapter 26. Real-Time Hardware and Systems Design Considerations
• Chapter 27. Epilogue--Perspectives in Vision
• Appendix A. Robust Statistics.

Computer and Machine Vision: Theory, Algorithms, Practicalities (previously entitled Machine Vision) clearly and systematically presents the basic methodology of computer and machine vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fourth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date tutorial text suitable for graduate students, researchers and R&D engineers working in this vibrant subject. Key features include: Practical examples and case studies give the `ins and outs' of developing real-world vision systems, giving engineers the realities of implementing the principles in practice New chapters containing case studies on surveillance and driver assistance systems give practical methods on these cutting-edge applications in computer vision Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples Updated content and new sections cover topics such as human iris location, image stitching, line detection using RANSAC, performance measures, and hyperspectral imaging The `recent developments' section now included in each chapter will be useful in bringing students and practitioners up to date with the subject.
(source: Nielsen Book Data)

• Medical Imaging
• Texture Analysis
• Image Segmentation
• Motion
• Deformable Models
• Document Analysis
• Pattern Analysis
• Tracking
• Object Recognition
• Machine Intelligence
• Machine Vision.
• (source: Nielsen Book Data)

This book is a collection of scientific papers published during the last five years, showing a broad spectrum of actual research topics and techniques used to solve challenging problems in the areas of computer vision and image analysis. The book will appeal to researchers, technicians and graduate students.
(source: Nielsen Book Data)

• 1. Vision, the Challenge
• Part 1 Low-Level Vision 2. Images and Imaging Operations 3. Basic Image Filtering Operations 4. Thresholding Techniques 5. Edge Detection 6. Binary Shape Analysis 7. Boundary Pattern Analysis 8. Mathematical Morphology
• Part 2 Intermediate-Level Vision 9. Line Detection 10. Circle Detection 11. The Hough Transform and Its Nature 12. Ellipse Detection 13. Hole Detection 14. Polygon and Corner Detection 15. Abstract Pattern Matching Techniques
• Part 3 3-D Vision and Motion 16. The Three-Dimensional World 17. Tackling the Perspective n-Point Problem 18. Motion 19. Invariants and their Applications 20. Egomotion and Related Tasks 21. Image Transformations and Camera Calibration
• Part 4 Towards Real-Time Pattern Recognition Systems 22. Automated Visual Inspection 23. Inspection of Cereal Grains 24. Statistical Pattern Recognition 25. Biologically Inspired Recognition Schemes 26. Texture 27. Image Acquisition 28. Real-Time Hardware and Systems Design Considerations
• Part 5 Perspectives on Vision 29. Machine Vision, Art or Science? Appendix A Robust Statistics.
• (source: Nielsen Book Data)

In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need. As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.
(source: Nielsen Book Data)

• Face Super-Resolution (W Zou & P C Yuen)
• Background Estimation and Its Applications (X-Da Chen & Y-H Yang)
• Topic on Video Analysis or Face Recognition (S Z Li)
• Fourier Methods for 3D Surface Modeling and Analysis (L Shen)
• Cellular Automata as a Tool for Image Processing and Pattern Recognition (P L Rosin & X-F Sun)
• Computer Vision and Pattern Recognition in Marine Fishery Identification and Monitoring (B-C Shen & C H Chen)
• A Performance Evaluation of Robot Localization Methods in Outdoor Terrains (E Fazl-Ersi & J K Tsotsos)
• Self Calibration of Camera Networks: Active and Passive Methods (J Denzler)
• Quasi-Dense Wide Baseline Matching for Three Views (J Kannala)
• and other papers.
• (source: Nielsen Book Data)

This book gives a comprehensive overview of the most advanced theories, methodologies and applications in computer vision. Particularly, it gives an extensive coverage of 3D and robotic vision problems. Example chapters featured are Fourier methods for 3D surface modeling and analysis, use of constraints for calibration-free 3D Euclidean reconstruction, novel photogeometric methods for capturing static and dynamic objects, performance evaluation of robot localization methods in outdoor terrains, integrating 3D vision with force/tactile sensors, tracking via in-floor sensing, self-calibration of camera networks, etc. Some unique applications of computer vision in marine fishery, biomedical issues, driver assistance, are also highlighted.
(source: Nielsen Book Data)

• Preface
• Accuracy of Face Recognition
• Extended 2-D PCA for Face Recognition: Analysis, Algorithms, & Performance Enhancement
• Face Recognition Employing PCA Based Artificial Immune Networks
• Distributed Face Recognition
• Facial Identity, Facial Emotion Recognition & Cognition in Remitted vs. Non-Remitted Patients with Schizophrenia
• Face Recognition: Different Encoding Methods on Newborn Infant Research
• Multi-Class Learning Facial Age Estimation with Fused Gabor & LBP Features
• Techniques of Frequency Domain Correlation for Face Recognition & its Photonic Implementation
• Forensic Face Recognition: A Survey
• Index.
• (source: Nielsen Book Data)

Face recognition has been an active area of research in image processing and computer vision due to its extensive range of prospective applications relating to biometrics, information security, video surveillance, law enforcement, identity authentication, smart cards, and access control systems. Topics discussed in this compilation include two-dimensional principal component analysis algorithms for face recognition; principal component analysis (PCA) and artificial immune networks in face recognition; multi-class learning facial age estimation and forensic face recognition.
(source: Nielsen Book Data)

• 1. Introduction to Fractals
• 2. Fractals and L-System
• 3. Interactive Generation of Fractals Images
• 4. Generation of a Class of Hybrid Fractals
• 5. L-System Strings from Ramification Matrix
• 6. 3D Modeling of Realistic Plants
• 7. Fractals Dimension
• 8. Research Directions of L-Systems.
• (source: Nielsen Book Data)

L-System Fractals covers all the fundamental aspects of generating fractals through L-system. Also it provides insight to various researches in this area for generating fractals through L-system approach & estimating dimensions. Also it discusses various applications of L-system fractals.
(source: Nielsen Book Data)

• Preface
• Image Processing: Characteristics & Applications in Textile Industry & Biomedicine
• Facial Expression Recognition Using Thermal Image Processing
• Applying Image Processing to In-Vitro Human Oocytes Characterization
• Matching of Infrared & Visual Images using the Fourier-Mellin Transform
• Comparison of Four Fractal Dimensions & Two Lacunarities to Assess the Trabecular Bone Architecture of ProximalFemur
• Image Processing for the Enhancement of Satellite Imagery
• Image Processing in the Analysis of Neuronal Dendritic Branching Patterns: Does Structure Follow Function Across Different Species?
• (source: Nielsen Book Data)

The use of image processing is presenting nowadays an exponential increase in the development of new solutions to deal with challenges in the industry and in the day-to-day life of Society. The improvements of technology enable the recurrent use of image processing based applications with a market which has reached mobile supporting devices such as smart phones and tablets. With the objective of discussing the main issues, challenges, opportunities and trends related to image processing, the result is a compilation of studies available to the readers allowing them to achieve new technological developments, present practical solutions and state-of-the-art technologies, provide guidance for future research and to create a connection between research and applications.
(source: Nielsen Book Data)