Editor Biographies xvii List of Contributors xxi Foreword xxvii Preface xxxi About the Companion website xxxv 1 Multilevel Image Segmentation UsingModified Genetic Algorithm (MfGA)-based Fuzzy C-Means 1Sourav De, Sunanda Das, Siddhartha Bhattacharyya, and Paramartha Dutta 1.1 Introduction 1 1.2 Fuzzy C-Means Algorithm 5 1.3 Modified Genetic Algorithms 6 1.4 Quality Evaluation Metrics for Image Segmentation 8 1.4.1 Correlation Coefficient 8 1.4.2 Empirical Measure Q(I) 8 1.5 MfGA-Based FCM Algorithm 9 1.6 Experimental Results and Discussion 11 1.7 Conclusion 22 References 22 2 Character Recognition Using Entropy-Based Fuzzy C-Means Clustering 25B. Kondalarao, S. Sahoo, and D.K. Pratihar 2.1 Introduction 25 2.2 Tools and Techniques Used 27 2.2.1 Fuzzy Clustering Algorithms 27 2.2.1.1 Fuzzy C-means Algorithm 28 2.2.1.2 Entropy-based Fuzzy Clustering 29 2.2.1.3 Entropy-based Fuzzy C-Means Algorithm 29 2.2.2 Sammon’s Nonlinear Mapping 30 2.3 Methodology 31 2.3.1 Data Collection 31 2.3.2 Preprocessing 31 2.3.3 Feature Extraction 32 2.3.4 Classification and Recognition 34 2.4 Results and Discussion 34 2.5 Conclusion and Future Scope ofWork 38 References 39 Appendix 41 3 A Two-Stage Approach to Handwritten Indic Script Identification 47Pawan Kumar Singh, Supratim Das, Ram Sarkar, andMita Nasipuri 3.1 Introduction 47 3.2 Review of RelatedWork 48 3.3 Properties of Scripts Used in the PresentWork 51 3.4 ProposedWork 52 3.4.1 DiscreteWavelet Transform 53 3.4.1.1 HaarWavelet Transform 55 3.4.2 Radon Transform (RT) 57 3.5 Experimental Results and Discussion 63 3.5.1 Evaluation of the Present Technique 65 3.5.1.1 Statistical Significance Tests 66 3.5.2 Statistical Performance Analysis of SVM Classifier 68 3.5.3 Comparison with Other RelatedWorks 71 3.5.4 Error Analysis 73 3.6 Conclusion 74 Acknowledgments 75 References 75 4 Feature Extraction and Segmentation Techniques in a Static Hand Gesture Recognition System 79Subhamoy Chatterjee, Piyush Bhandari, and Mahesh Kumar Kolekar 4.1 Introduction 79 4.2 Segmentation Techniques 81 4.2.1 Otsu Method for Gesture Segmentation 81 4.2.2 Color Space–Based Models for Hand Gesture Segmentation 82 4.2.2.1 RGB Color Space–Based Segmentation 82 4.2.2.2 HSI Color Space–Based Segmentation 83 4.2.2.3 YCbCr Color Space–Based Segmentation 83 4.2.2.4 YIQ Color Space–Based Segmentation 83 4.2.3 Robust Skin Color Region Detection Using K-Means Clustering and Mahalanobish Distance 84 4.2.3.1 Rotation Normalization 85 4.2.3.2 Illumination Normalization 85 4.2.3.3 Morphological Filtering 85 4.3 Feature Extraction Techniques 86 4.3.1 Theory of Moment Features 86 4.3.2 Contour-Based Features 88 4.4 State of the Art of Static Hand Gesture Recognition Techniques 89 4.4.1 Zoning Methods 90 4.4.2 F-Ratio-BasedWeighted Feature Extraction 90 4.4.3 Feature Fusion Techniques 91 4.5 Results and Discussion 92 4.5.1 Segmentation Result 93 4.5.2 Feature Extraction Result 94 4.6 Conclusion 97 4.6.1 FutureWork 99 Acknowledgment 99 References 99 5 SVM Combination for an Enhanced Prediction ofWriters’ Soft Biometrics 103Nesrine Bouadjenek, Hassiba Nemmour, and Youcef Chibani 5.1 Introduction 103 5.2 Soft Biometrics and Handwriting Over Time 104 5.3 Soft Biometrics Prediction System 106 5.3.1 Feature Extraction 107 5.3.1.1 Local Binary Patterns 107 5.3.1.2 Histogram of Oriented Gradients 108 5.3.1.3 Gradient Local Binary Patterns 108 5.3.2 Classification 109 5.3.3 Fuzzy Integrals–Based Combination Classifier 111 5.3.3.1 g�� Fuzzy Measure 111 5.3.3.2 Sugeno’s Fuzzy Integral 113 5.3.3.3 Fuzzy Min-Max 113 5.4 Experimental Evaluation 113 5.4.1 Data Sets 113 5.4.1.1 IAM Data Set 113 5.4.1.2 KHATT Data Set 114 5.4.2 Experimental Setting 114 5.4.3 Gender Prediction Results 117 5.4.4 Handedness Prediction Results 117 5.4.5 Age Prediction Results 118 5.5 Discussion and Performance Comparison 118 5.6 Conclusion 120 References 121 6 Brain-Inspired Machine Intelligence for Image Analysis: Convolutional Neural Networks 127Siddharth Srivastava and Brejesh Lall 6.1 Introduction 127 6.2 Convolutional Neural Networks 129 6.2.1 Building Blocks 130 6.2.1.1 Perceptron 134 6.2.2 Learning 135 6.2.2.1 Gradient Descent 136 6.2.2.2 Back-Propagation 136 6.2.3 Convolution 139 6.2.4 Convolutional Neural Networks:The Architecture 141 6.2.4.1 Convolution Layer 142 6.2.4.2 Pooling Layer 145 6.2.4.3 Dense or Fully Connected Layer 146 6.2.5 Considerations in Implementation of CNNs 146 6.2.6 CNN in Action 147 6.2.7 Tools for Convolutional Neural Networks 148 6.2.8 CNN Coding Examples 148 6.2.8.1 MatConvNet 148 6.2.8.2 Visualizing a CNN 149 6.2.8.3 Image Category Classification Using Deep Learning 153 6.3 Toward Understanding the Brain, CNNs, and Images 157 6.3.1 Applications 157 6.3.2 Case Studies 158 6.4 Conclusion 159 References 159 7 Human Behavioral Analysis Using Evolutionary Algorithms and Deep Learning 165Earnest Paul Ijjina and Chalavadi Krishna Mohan 7.1 Introduction 165 7.2 Human Action Recognition Using Evolutionary Algorithms and Deep Learning 167 7.2.1 Evolutionary Algorithms for Search Optimization 168 7.2.2 Action Bank Representation for Action Recognition 168 7.2.3 Deep Convolutional Neural Network for Human Action Recognition 169 7.2.4 CNN Classifier Optimized Using Evolutionary Algorithms 170 7.3 Experimental Study 170 7.3.1 Evaluation on the UCF50 Data Set 170 7.3.2 Evaluation on the KTH Video Data Set 172 7.3.3 Analysis and Discussion 176 7.3.4 Experimental Setup and Parameter Optimization 177 7.3.5 Computational Complexity 182 7.4 Conclusions and FutureWork 183 References 183 8 Feature-Based Robust Description andMonocular Detection: An Application to Vehicle Tracking 187Ramazan Yíldíz and Tankut Acarman 8.1 Introduction 187 8.2 Extraction of Local Features by SIFT and SURF 188 8.3 Global Features: Real-Time Detection and Vehicle Tracking 190 8.4 Vehicle Detection and Validation 194 8.4.1 X-Analysis 194 8.4.2 Horizontal Prominent Line Frequency Analysis 195 8.4.3 Detection History 196 8.5 Experimental Study 197 8.5.1 Local Features Assessment 197 8.5.2 Global Features Assessment 197 8.5.3 Local versus Global Features Assessment 201 8.6 Conclusions 201 References 202 9 A GIS Anchored Technique for Social Utility Hotspot Detection 205Anirban Chakraborty, J.K.Mandal, Arnab Patra, and JayatraMajumdar 9.1 Introduction 205 9.2 The Technique 207 9.3 Case Study 209 9.4 Implementation and Results 221 9.5 Analysis and Comparisons 224 9.6 Conclusions 229 Acknowledgments 229 References 230 10 Hyperspectral Data Processing: Spectral Unmixing, Classification, and Target Identification 233Vaibhav Lodhi, Debashish Chakravarty, and PabitraMitra 10.1 Introduction 233 10.2 Background and Hyperspectral Imaging System 234 10.3 Overview of Hyperspectral Image Processing 236 10.3.1 Image Acquisition 237 10.3.2 Calibration 237 10.3.3 Spatial and Spectral preprocessing 238 10.3.4 Dimension Reduction 239 10.3.4.1 Transformation-Based Approaches 239 10.3.4.2 Selection-Based Approaches 239 10.3.5 postprocessing 240 10.4 Spectral Unmixing 240 10.4.1 Unmixing Processing Chain 240 10.4.2 Mixing Model 241 10.4.2.1 Linear Mixing Model (LMM) 242 10.4.2.2 Nonlinear Mixing Model 242 10.4.3 Geometrical-Based Approaches to Linear Spectral Unmixing 243 10.4.3.1 Pure Pixel-Based Techniques 243 10.4.3.2 Minimum Volume-Based Techniques 244 10.4.4 Statistics-Based Approaches 244 10.4.5 Sparse Regression-Based Approach 245 10.4.5.1 Moore–Penrose Pseudoinverse (MPP) 245 10.4.5.2 Orthogonal Matching Pursuit (OMP) 246 10.4.5.3 Iterative Spectral Mixture Analysis (ISMA) 246 10.4.6 Hybrid Techniques 246 10.5 Classification 247 10.5.1 Feature Mining 247 10.5.1.1 Feature Selection (FS) 248 10.5.1.2 Feature Extraction 248 10.5.2 Supervised Classification 248 10.5.2.1 Minimum Distance Classifier 249 10.5.2.2 Maximum Likelihood Classifier (MLC) 250 10.5.2.3 Support Vector Machines (SVMs) 250 10.5.3 Hybrid Techniques 250 10.6 Target Detection 251 10.6.1 Anomaly Detection 251 10.6.1.1 RX Anomaly Detection 252 10.6.1.2 Subspace-Based Anomaly Detection 253 10.6.2 Signature-Based Target Detection 253 10.6.2.1 Euclidean distance 254 10.6.2.2 Spectral Angle Mapper (SAM) 254 10.6.2.3 Spectral Matched Vilter (SMF) 254 10.6.2.4 Matched Subspace Detector (MSD) 255 10.6.3 Hybrid Techniques 255 10.7 Conclusions 256 References 256 11 A Hybrid Approach for Band Selection of Hyperspectral Images 263Aditi Roy Chowdhury, Joydev Hazra, and Paramartha Dutta 11.1 Introduction 263 11.2 Relevant Concept Revisit 266 11.2.1 Feature Extraction 266 11.2.2 Feature Selection Using 2D PCA 266 11.2.3 Immune Clonal System 267 11.2.4 Fuzzy KNN 268 11.3 Proposed Algorithm 271 11.4 Experiment and Result 271 11.4.1 Description of the Data Set 272 11.4.2 Experimental Details 274 11.4.3 Analysis of Results 275 11.5 Conclusion 278 References 279 12 Uncertainty-Based Clustering Algorithms for Medical Image Analysis 283Deepthi P. Hudedagaddi and B.K. Tripathy 12.1 Introduction 283 12.2 Uncertainty-Based Clustering Algorithms 283 12.2.1 Fuzzy C-Means 284 12.2.2 Rough Fuzzy C-Means 285 12.2.3 Intuitionistic Fuzzy C-Means 285 12.2.4 Rough Intuitionistic Fuzzy C-Means 286 12.3 Image Processing 286 12.4 Medical Image Analysis with Uncertainty-Based Clustering Algorithms 287 12.4.1 FCM with Spatial Information for Image Segmentation 287 12.4.2 Fast and Robust FCM Incorporating Local Information for Image Segmentation 290 12.4.3 Image Segmentation Using Spatial IFCM 291 12.4.3.1 Applications of Spatial FCM and Spatial IFCM on Leukemia Images 292 12.5 Conclusions 293 References 293 13 An Optimized Breast Cancer Diagnosis SystemUsing a Cuckoo Search Algorithm and Support Vector Machine Classifier 297Manoharan Prabukumar, Loganathan Agilandeeswari, and Arun Kumar Sangaiah 13.1 Introduction 297 13.2 Technical Background 301 13.2.1 Morphological Segmentation 301 13.2.2 Cuckoo Search Optimization Algorithm 302 13.2.3 Support Vector Machines 303 13.3 Proposed Breast Cancer Diagnosis System 303 13.3.1 Preprocessing of Breast Cancer Image 303 13.3.2 Feature Extraction 304 13.3.2.1 Geometric Features 304 13.3.2.2 Texture Features 305 13.3.2.3 Statistical Features 306 13.3.3 Features Selection 306 13.3.4 Features Classification 307 13.4 Results and Discussions 307 13.5 Conclusion 310 13.6 FutureWork 310 References 310 14 Analysis of Hand Vein Images Using Hybrid Techniques 315R. Sudhakar, S. Bharathi, and V. Gurunathan 14.1 Introduction 315 14.2 Analysis of Vein Images in the Spatial Domain 318 14.2.1 Preprocessing 318 14.2.2 Feature Extraction 319 14.2.3 Feature-Level Fusion 320 14.2.4 Score Level Fusion 320 14.2.5 Results and Discussion 322 14.2.5.1 Evaluation Metrics 323 14.3 Analysis of Vein Images in the Frequency Domain 326 14.3.1 Preprocessing 326 14.3.2 Feature Extraction 326 14.3.3 Feature-Level Fusion 330 14.3.4 Support Vector Machine Classifier 331 14.3.5 Results and Discussion 331 14.4 Comparative Analysis of Spatial and Frequency Domain Systems 332 14.5 Conclusion 335 References 335 15 Identification of Abnormal Masses in Digital Mammogram Using Statistical Decision Making 339Indra Kanta Maitra and Samir Kumar Bandyopadhyay 15.1 Introduction 339 15.1.1 Breast Cancer 339 15.1.2 Computer-Aided Detection/Diagnosis (CAD) 340 15.1.3 Segmentation 340 15.2 PreviousWorks 341 15.3 Proposed Method 343 15.3.1 Preparation 343 15.3.2 Preprocessing 345 15.3.2.1 Image Enhancement and Edge Detection 346 15.3.2.2 Isolation and Suppression of Pectoral Muscle 348 15.3.2.3 Breast Contour Detection 351 15.3.2.4 Anatomical Segmentation 353 15.3.3 Identification of Abnormal Region(s) 354 15.3.3.1 Coloring of Regions 354 15.3.3.2 Statistical Decision Making 355 15.4 Experimental Result 358 15.4.1 Case Study with Normal Mammogram 358 15.4.2 Case Study with Abnormalities Embedded in Fatty Tissues 358 15.4.3 Case Study with Abnormalities Embedded in Fatty-Fibro-Glandular Tissues 359 15.4.4 Case Study with Abnormalities Embedded in Dense-Fibro-Glandular Tissues 359 15.5 Result Evaluation 360 15.5.1 Statistical Analysis 361 15.5.2 ROC Analysis 361 15.5.3 Accuracy Estimation 365 15.6 Comparative Analysis 366 15.7 Conclusion 366 Acknowledgments 366 References 367 16 Automatic Detection of Coronary Artery Stenosis Using Bayesian Classification and Gaussian Filters Based on Differential Evolution 369Ivan Cruz-Aceves, Fernando Cervantes-Sanchez, and Arturo Hernandez-Aguirre 16.1 Introduction 369 16.2 Background 370 16.2.1 Gaussian Matched Filters 371 16.2.2 Differential Evolution 371 16.2.2.1 Example: Global Optimization of the Ackley Function 373 16.2.3 Bayesian Classification 375 16.2.3.1 Example: Classification Problem 375 16.3 Proposed Method 377 16.3.1 Optimal Parameter Selection of GMF Using Differential Evolution 377 16.3.2 Thresholding of the Gaussian Filter Response 378 16.3.3 Stenosis Detection Using Second-Order Derivatives 378 16.3.4 Stenosis Detection Using Bayesian Classification 379 16.4 Computational Experiments 381 16.4.1 Results of Vessel Detection 382 16.4.2 Results of Vessel Segmentation 382 16.4.3 Evaluation of Detection of Coronary Artery Stenosis 384 16.5 Concluding Remarks 386 Acknowledgment 388 References 388 17 Evaluating the Efficacy of Multi-resolution Texture Features for Prediction of Breast Density UsingMammographic Images 391Kriti, Harleen Kaur, and Jitendra Virmani 17.1 Introduction 391 17.1.1 Comparison of Related Methods with the Proposed Method 397 17.2 Materials and Methods 398 17.2.1 Description of Database 398 17.2.2 ROI Extraction Protocol 398 17.2.3 Workflow for CAD System Design 398 17.2.3.1 Feature Extraction 400 17.2.3.2 Classification 407 17.3 Results 410 17.3.1 Results Based on Classification Performance of the Classifiers (Classification Accuracy and Sensitivity) for Each Class 411 17.3.1.1 Experiment I: To Determine the Performance of Different FDVs Using SVM Classifier 411 17.3.1.2 Experiment II: To Determine the Performance of Different FDVs Using SSVM Classifier 412 17.3.2 Results Based on Computational Efficiency of Classifiers for Predicting 161 Instances of Testing Dataset 412 17.4 Conclusion and Future Scope 413 References 415 Index 423
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