PROF. (DR.) SIDDHARTHA BHATTACHARYYA (SMIEEE, SMACM, LMCSI, LMOSI, LMISTE, MIAENG, MIRSS, MACSE, MIAASSE) obtained his Bachelors in Physics, Optics and Optoelectronics and Masters in Optics and Optoelectronics from the University of Calcutta, India, in 1995, 1998 and 2000 respectively. He completed a PhD in Computer Science and Engineering from Jadavpur University, India, in 2008. He is currently the Professor and Head of Information Technology at the RCC Institute of Information Technology, Kolkata, India. He is also the Dean (Research & Development) of the institute. He is a co-author of 3 books and co-editor of 5 books and more than 135 research publications.

DR. INDRAJIT PAN did his Bachelors in Computer Science Engineering in 2005 at The University of Burdwan, India, and completed his Masters in Information Technology at Bengal Engineering and Science University, Shibpur. He got a University Medal for his performance in his Masters. Later, he was awarded a PhD in Engineering from the Indian Institute of Engineering, Science and Technology (IIEST). He has more than 10 years' experience teaching in undergraduate and postgraduate engineering in IT and allied field. Currently, he is an Assistant Professor of Information Technology at the RCC Institute of Information Technology. His research interests include CAD, Computer Security, Soft Computing Applications and Cloud Computing.

DR. ANIRBAN MUKHERJEE did his Bachelors in Civil Engineering in 1994 at Jadavpur University, Kolkata. He completed his PhD on 'Automatic Diagram Drawing based on Natural Language Text Understanding' at the Indian Institute of Engineering, Science and Technology (IIEST), Shibpur, in 2014. He has more than 20 years' experience in teaching undergraduate and postgraduate engineering in IT and allied field. Currently, he is an Associate Professor and HOD of Engineering Science & Management at the RCC Institute of Information Technology. He has experience of working in computer aided design and engineering analysis and also of teaching on CAD courses. His research interests include Computer Graphics & CAD, Soft Computing Applications and Assistive Technology. He has co-authored two UG engineering textbooks: a popular one on 'Computer Graphics and Multimedia' and another on 'Engineering Mechanics'. He has also co-authored more than 15 books on Computer Graphics/Multimedia for distance learning professional courses at different Universities in India.

PROF. (DR.) PARAMARTHA DUTTA has a B. Stat. (Hons.), M. Stat., M. Tech in Computer Science, and a PhD (Engineering) in Computer Science and Technology. With around 23 years of research and academic experience, Professor Dutta is currently serving as a Professor in the Department of Computer and System Sciences, Visva Bharati University. Professor Dutta is a senior Member of IEEE and ACM. He has executed almost 200 projects funded by the Govt. of India. Professor Dutta has remained associated with various Universities and Institutes as Visiting/Guest faculty. To date, Professor Dutta has more than 6 authored and 6 edited books in addition to around 180 papers, published in different International Journals and in International/National conference proceedings.

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 1
Sourav 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 25
B. 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 47
Pawan 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 79
Subhamoy 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 103
Nesrine 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 127
Siddharth 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 165
Earnest 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 187
Ramazan 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 205
Anirban 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 233
Vaibhav 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 263
Aditi 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 283
Deepthi 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 297
Manoharan 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 315
R. 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 339
Indra 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 369
Ivan 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 391
Kriti, 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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