{"product_id":"hybrid-intelligence-for-image-analysis-and-understanding-isbn-9781119242925","title":"Hybrid Intelligence for Image Analysis and Understanding","description":"\u003cp\u003e\u003cb\u003eA synergy of techniques on hybrid intelligence for real-life image analysis\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHybrid Intelligence for Image Analysis and Understanding\u003c\/i\u003e brings together research on the latest results and progress in the development of hybrid intelligent techniques for faithful image analysis and understanding. As such, the focus is on the methods of computational intelligence, with an emphasis on hybrid intelligent methods applied to image analysis and understanding.\u003c\/p\u003e \u003cp\u003eThe book offers a diverse range of hybrid intelligence techniques under the umbrellas of image thresholding, image segmentation, image analysis and video analysis.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides in-depth analysis of hybrid intelligent paradigms.\u003c\/li\u003e \u003cli\u003eDivided into self-contained chapters.\u003c\/li\u003e \u003cli\u003eProvides ample case studies, illustrations and photographs of real-life examples to illustrate findings and applications of different hybrid intelligent paradigms.\u003c\/li\u003e \u003cli\u003eOffers new solutions to recent problems in computer science, specifically in the application of hybrid intelligent techniques for image analysis and understanding, using well-known contemporary algorithms.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is essential reading for lecturers, researchers and graduate students in electrical engineering and computer science.\u003c\/p\u003e \u003cp\u003eEditor Biographies xvii\u003c\/p\u003e \u003cp\u003eList of Contributors xxi\u003c\/p\u003e \u003cp\u003eForeword xxvii\u003c\/p\u003e \u003cp\u003ePreface xxxi\u003c\/p\u003e \u003cp\u003eAbout the Companion website xxxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Multilevel Image Segmentation UsingModified Genetic Algorithm (MfGA)-based Fuzzy C-Means 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSourav De, Sunanda Das, Siddhartha Bhattacharyya, and Paramartha Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Fuzzy C-Means Algorithm 5\u003c\/p\u003e \u003cp\u003e1.3 Modified Genetic Algorithms 6\u003c\/p\u003e \u003cp\u003e1.4 Quality Evaluation Metrics for Image Segmentation 8\u003c\/p\u003e \u003cp\u003e1.4.1 Correlation Coefficient 8\u003c\/p\u003e \u003cp\u003e1.4.2 Empirical Measure Q(I) 8\u003c\/p\u003e \u003cp\u003e1.5 MfGA-Based FCM Algorithm 9\u003c\/p\u003e \u003cp\u003e1.6 Experimental Results and Discussion 11\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 22\u003c\/p\u003e \u003cp\u003eReferences 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Character Recognition Using Entropy-Based Fuzzy C-Means Clustering 25\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eB. Kondalarao, S. Sahoo, and D.K. Pratihar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Tools and Techniques Used 27\u003c\/p\u003e \u003cp\u003e2.2.1 Fuzzy Clustering Algorithms 27\u003c\/p\u003e \u003cp\u003e2.2.1.1 Fuzzy C-means Algorithm 28\u003c\/p\u003e \u003cp\u003e2.2.1.2 Entropy-based Fuzzy Clustering 29\u003c\/p\u003e \u003cp\u003e2.2.1.3 Entropy-based Fuzzy C-Means Algorithm 29\u003c\/p\u003e \u003cp\u003e2.2.2 Sammon’s Nonlinear Mapping 30\u003c\/p\u003e \u003cp\u003e2.3 Methodology 31\u003c\/p\u003e \u003cp\u003e2.3.1 Data Collection 31\u003c\/p\u003e \u003cp\u003e2.3.2 Preprocessing 31\u003c\/p\u003e \u003cp\u003e2.3.3 Feature Extraction 32\u003c\/p\u003e \u003cp\u003e2.3.4 Classification and Recognition 34\u003c\/p\u003e \u003cp\u003e2.4 Results and Discussion 34\u003c\/p\u003e \u003cp\u003e2.5 Conclusion and Future Scope ofWork 38\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003eAppendix 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Two-Stage Approach to Handwritten Indic Script Identification 47\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePawan Kumar Singh, Supratim Das, Ram Sarkar, andMita Nasipuri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 Review of RelatedWork 48\u003c\/p\u003e \u003cp\u003e3.3 Properties of Scripts Used in the PresentWork 51\u003c\/p\u003e \u003cp\u003e3.4 ProposedWork 52\u003c\/p\u003e \u003cp\u003e3.4.1 DiscreteWavelet Transform 53\u003c\/p\u003e \u003cp\u003e3.4.1.1 HaarWavelet Transform 55\u003c\/p\u003e \u003cp\u003e3.4.2 Radon Transform (RT) 57\u003c\/p\u003e \u003cp\u003e3.5 Experimental Results and Discussion 63\u003c\/p\u003e \u003cp\u003e3.5.1 Evaluation of the Present Technique 65\u003c\/p\u003e \u003cp\u003e3.5.1.1 Statistical Significance Tests 66\u003c\/p\u003e \u003cp\u003e3.5.2 Statistical Performance Analysis of SVM Classifier 68\u003c\/p\u003e \u003cp\u003e3.5.3 Comparison with Other RelatedWorks 71\u003c\/p\u003e \u003cp\u003e3.5.4 Error Analysis 73\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 74\u003c\/p\u003e \u003cp\u003eAcknowledgments 75\u003c\/p\u003e \u003cp\u003eReferences 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Feature Extraction and Segmentation Techniques in a Static Hand Gesture Recognition System 79\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSubhamoy Chatterjee, Piyush Bhandari, and Mahesh Kumar Kolekar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 79\u003c\/p\u003e \u003cp\u003e4.2 Segmentation Techniques 81\u003c\/p\u003e \u003cp\u003e4.2.1 Otsu Method for Gesture Segmentation 81\u003c\/p\u003e \u003cp\u003e4.2.2 Color Space–Based Models for Hand Gesture Segmentation 82\u003c\/p\u003e \u003cp\u003e4.2.2.1 RGB Color Space–Based Segmentation 82\u003c\/p\u003e \u003cp\u003e4.2.2.2 HSI Color Space–Based Segmentation 83\u003c\/p\u003e \u003cp\u003e4.2.2.3 YCbCr Color Space–Based Segmentation 83\u003c\/p\u003e \u003cp\u003e4.2.2.4 YIQ Color Space–Based Segmentation 83\u003c\/p\u003e \u003cp\u003e4.2.3 Robust Skin Color Region Detection Using K-Means Clustering and Mahalanobish Distance 84\u003c\/p\u003e \u003cp\u003e4.2.3.1 Rotation Normalization 85\u003c\/p\u003e \u003cp\u003e4.2.3.2 Illumination Normalization 85\u003c\/p\u003e \u003cp\u003e4.2.3.3 Morphological Filtering 85\u003c\/p\u003e \u003cp\u003e4.3 Feature Extraction Techniques 86\u003c\/p\u003e \u003cp\u003e4.3.1 Theory of Moment Features 86\u003c\/p\u003e \u003cp\u003e4.3.2 Contour-Based Features 88\u003c\/p\u003e \u003cp\u003e4.4 State of the Art of Static Hand Gesture Recognition Techniques 89\u003c\/p\u003e \u003cp\u003e4.4.1 Zoning Methods 90\u003c\/p\u003e \u003cp\u003e4.4.2 F-Ratio-BasedWeighted Feature Extraction 90\u003c\/p\u003e \u003cp\u003e4.4.3 Feature Fusion Techniques 91\u003c\/p\u003e \u003cp\u003e4.5 Results and Discussion 92\u003c\/p\u003e \u003cp\u003e4.5.1 Segmentation Result 93\u003c\/p\u003e \u003cp\u003e4.5.2 Feature Extraction Result 94\u003c\/p\u003e \u003cp\u003e4.6 Conclusion 97\u003c\/p\u003e \u003cp\u003e4.6.1 FutureWork 99\u003c\/p\u003e \u003cp\u003eAcknowledgment 99\u003c\/p\u003e \u003cp\u003eReferences 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 SVM Combination for an Enhanced Prediction ofWriters’ Soft Biometrics 103\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNesrine Bouadjenek, Hassiba Nemmour, and Youcef Chibani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 103\u003c\/p\u003e \u003cp\u003e5.2 Soft Biometrics and Handwriting Over Time 104\u003c\/p\u003e \u003cp\u003e5.3 Soft Biometrics Prediction System 106\u003c\/p\u003e \u003cp\u003e5.3.1 Feature Extraction 107\u003c\/p\u003e \u003cp\u003e5.3.1.1 Local Binary Patterns 107\u003c\/p\u003e \u003cp\u003e5.3.1.2 Histogram of Oriented Gradients 108\u003c\/p\u003e \u003cp\u003e5.3.1.3 Gradient Local Binary Patterns 108\u003c\/p\u003e \u003cp\u003e5.3.2 Classification 109\u003c\/p\u003e \u003cp\u003e5.3.3 Fuzzy Integrals–Based Combination Classifier 111\u003c\/p\u003e \u003cp\u003e5.3.3.1 g�� Fuzzy Measure 111\u003c\/p\u003e \u003cp\u003e5.3.3.2 Sugeno’s Fuzzy Integral 113\u003c\/p\u003e \u003cp\u003e5.3.3.3 Fuzzy Min-Max 113\u003c\/p\u003e \u003cp\u003e5.4 Experimental Evaluation 113\u003c\/p\u003e \u003cp\u003e5.4.1 Data Sets 113\u003c\/p\u003e \u003cp\u003e5.4.1.1 IAM Data Set 113\u003c\/p\u003e \u003cp\u003e5.4.1.2 KHATT Data Set 114\u003c\/p\u003e \u003cp\u003e5.4.2 Experimental Setting 114\u003c\/p\u003e \u003cp\u003e5.4.3 Gender Prediction Results 117\u003c\/p\u003e \u003cp\u003e5.4.4 Handedness Prediction Results 117\u003c\/p\u003e \u003cp\u003e5.4.5 Age Prediction Results 118\u003c\/p\u003e \u003cp\u003e5.5 Discussion and Performance Comparison 118\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 120\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Brain-Inspired Machine Intelligence for Image Analysis: Convolutional Neural Networks 127\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSiddharth Srivastava and Brejesh Lall\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 127\u003c\/p\u003e \u003cp\u003e6.2 Convolutional Neural Networks 129\u003c\/p\u003e \u003cp\u003e6.2.1 Building Blocks 130\u003c\/p\u003e \u003cp\u003e6.2.1.1 Perceptron 134\u003c\/p\u003e \u003cp\u003e6.2.2 Learning 135\u003c\/p\u003e \u003cp\u003e6.2.2.1 Gradient Descent 136\u003c\/p\u003e \u003cp\u003e6.2.2.2 Back-Propagation 136\u003c\/p\u003e \u003cp\u003e6.2.3 Convolution 139\u003c\/p\u003e \u003cp\u003e6.2.4 Convolutional Neural Networks:The Architecture 141\u003c\/p\u003e \u003cp\u003e6.2.4.1 Convolution Layer 142\u003c\/p\u003e \u003cp\u003e6.2.4.2 Pooling Layer 145\u003c\/p\u003e \u003cp\u003e6.2.4.3 Dense or Fully Connected Layer 146\u003c\/p\u003e \u003cp\u003e6.2.5 Considerations in Implementation of CNNs 146\u003c\/p\u003e \u003cp\u003e6.2.6 CNN in Action 147\u003c\/p\u003e \u003cp\u003e6.2.7 Tools for Convolutional Neural Networks 148\u003c\/p\u003e \u003cp\u003e6.2.8 CNN Coding Examples 148\u003c\/p\u003e \u003cp\u003e6.2.8.1 MatConvNet 148\u003c\/p\u003e \u003cp\u003e6.2.8.2 Visualizing a CNN 149\u003c\/p\u003e \u003cp\u003e6.2.8.3 Image Category Classification Using Deep Learning 153\u003c\/p\u003e \u003cp\u003e6.3 Toward Understanding the Brain, CNNs, and Images 157\u003c\/p\u003e \u003cp\u003e6.3.1 Applications 157\u003c\/p\u003e \u003cp\u003e6.3.2 Case Studies 158\u003c\/p\u003e \u003cp\u003e6.4 Conclusion 159\u003c\/p\u003e \u003cp\u003eReferences 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Human Behavioral Analysis Using Evolutionary Algorithms and Deep Learning 165\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEarnest Paul Ijjina and Chalavadi Krishna Mohan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 165\u003c\/p\u003e \u003cp\u003e7.2 Human Action Recognition Using Evolutionary Algorithms and Deep Learning 167\u003c\/p\u003e \u003cp\u003e7.2.1 Evolutionary Algorithms for Search Optimization 168\u003c\/p\u003e \u003cp\u003e7.2.2 Action Bank Representation for Action Recognition 168\u003c\/p\u003e \u003cp\u003e7.2.3 Deep Convolutional Neural Network for Human Action Recognition 169\u003c\/p\u003e \u003cp\u003e7.2.4 CNN Classifier Optimized Using Evolutionary Algorithms 170\u003c\/p\u003e \u003cp\u003e7.3 Experimental Study 170\u003c\/p\u003e \u003cp\u003e7.3.1 Evaluation on the UCF50 Data Set 170\u003c\/p\u003e \u003cp\u003e7.3.2 Evaluation on the KTH Video Data Set 172\u003c\/p\u003e \u003cp\u003e7.3.3 Analysis and Discussion 176\u003c\/p\u003e \u003cp\u003e7.3.4 Experimental Setup and Parameter Optimization 177\u003c\/p\u003e \u003cp\u003e7.3.5 Computational Complexity 182\u003c\/p\u003e \u003cp\u003e7.4 Conclusions and FutureWork 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Feature-Based Robust Description andMonocular Detection: An Application to Vehicle Tracking 187\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRamazan Yíldíz and Tankut Acarman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 187\u003c\/p\u003e \u003cp\u003e8.2 Extraction of Local Features by SIFT and SURF 188\u003c\/p\u003e \u003cp\u003e8.3 Global Features: Real-Time Detection and Vehicle Tracking 190\u003c\/p\u003e \u003cp\u003e8.4 Vehicle Detection and Validation 194\u003c\/p\u003e \u003cp\u003e8.4.1 X-Analysis 194\u003c\/p\u003e \u003cp\u003e8.4.2 Horizontal Prominent Line Frequency Analysis 195\u003c\/p\u003e \u003cp\u003e8.4.3 Detection History 196\u003c\/p\u003e \u003cp\u003e8.5 Experimental Study 197\u003c\/p\u003e \u003cp\u003e8.5.1 Local Features Assessment 197\u003c\/p\u003e \u003cp\u003e8.5.2 Global Features Assessment 197\u003c\/p\u003e \u003cp\u003e8.5.3 Local versus Global Features Assessment 201\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 201\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 A GIS Anchored Technique for Social Utility Hotspot Detection 205\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnirban Chakraborty, J.K.Mandal, Arnab Patra, and JayatraMajumdar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 205\u003c\/p\u003e \u003cp\u003e9.2 The Technique 207\u003c\/p\u003e \u003cp\u003e9.3 Case Study 209\u003c\/p\u003e \u003cp\u003e9.4 Implementation and Results 221\u003c\/p\u003e \u003cp\u003e9.5 Analysis and Comparisons 224\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 229\u003c\/p\u003e \u003cp\u003eAcknowledgments 229\u003c\/p\u003e \u003cp\u003eReferences 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Hyperspectral Data Processing: Spectral Unmixing, Classification, and Target Identification 233\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVaibhav Lodhi, Debashish Chakravarty, and PabitraMitra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 233\u003c\/p\u003e \u003cp\u003e10.2 Background and Hyperspectral Imaging System 234\u003c\/p\u003e \u003cp\u003e10.3 Overview of Hyperspectral Image Processing 236\u003c\/p\u003e \u003cp\u003e10.3.1 Image Acquisition 237\u003c\/p\u003e \u003cp\u003e10.3.2 Calibration 237\u003c\/p\u003e \u003cp\u003e10.3.3 Spatial and Spectral preprocessing 238\u003c\/p\u003e \u003cp\u003e10.3.4 Dimension Reduction 239\u003c\/p\u003e \u003cp\u003e10.3.4.1 Transformation-Based Approaches 239\u003c\/p\u003e \u003cp\u003e10.3.4.2 Selection-Based Approaches 239\u003c\/p\u003e \u003cp\u003e10.3.5 postprocessing 240\u003c\/p\u003e \u003cp\u003e10.4 Spectral Unmixing 240\u003c\/p\u003e \u003cp\u003e10.4.1 Unmixing Processing Chain 240\u003c\/p\u003e \u003cp\u003e10.4.2 Mixing Model 241\u003c\/p\u003e \u003cp\u003e10.4.2.1 Linear Mixing Model (LMM) 242\u003c\/p\u003e \u003cp\u003e10.4.2.2 Nonlinear Mixing Model 242\u003c\/p\u003e \u003cp\u003e10.4.3 Geometrical-Based Approaches to Linear Spectral Unmixing 243\u003c\/p\u003e \u003cp\u003e10.4.3.1 Pure Pixel-Based Techniques 243\u003c\/p\u003e \u003cp\u003e10.4.3.2 Minimum Volume-Based Techniques 244\u003c\/p\u003e \u003cp\u003e10.4.4 Statistics-Based Approaches 244\u003c\/p\u003e \u003cp\u003e10.4.5 Sparse Regression-Based Approach 245\u003c\/p\u003e \u003cp\u003e10.4.5.1 Moore–Penrose Pseudoinverse (MPP) 245\u003c\/p\u003e \u003cp\u003e10.4.5.2 Orthogonal Matching Pursuit (OMP) 246\u003c\/p\u003e \u003cp\u003e10.4.5.3 Iterative Spectral Mixture Analysis (ISMA) 246\u003c\/p\u003e \u003cp\u003e10.4.6 Hybrid Techniques 246\u003c\/p\u003e \u003cp\u003e10.5 Classification 247\u003c\/p\u003e \u003cp\u003e10.5.1 Feature Mining 247\u003c\/p\u003e \u003cp\u003e10.5.1.1 Feature Selection (FS) 248\u003c\/p\u003e \u003cp\u003e10.5.1.2 Feature Extraction 248\u003c\/p\u003e \u003cp\u003e10.5.2 Supervised Classification 248\u003c\/p\u003e \u003cp\u003e10.5.2.1 Minimum Distance Classifier 249\u003c\/p\u003e \u003cp\u003e10.5.2.2 Maximum Likelihood Classifier (MLC) 250\u003c\/p\u003e \u003cp\u003e10.5.2.3 Support Vector Machines (SVMs) 250\u003c\/p\u003e \u003cp\u003e10.5.3 Hybrid Techniques 250\u003c\/p\u003e \u003cp\u003e10.6 Target Detection 251\u003c\/p\u003e \u003cp\u003e10.6.1 Anomaly Detection 251\u003c\/p\u003e \u003cp\u003e10.6.1.1 RX Anomaly Detection 252\u003c\/p\u003e \u003cp\u003e10.6.1.2 Subspace-Based Anomaly Detection 253\u003c\/p\u003e \u003cp\u003e10.6.2 Signature-Based Target Detection 253\u003c\/p\u003e \u003cp\u003e10.6.2.1 Euclidean distance 254\u003c\/p\u003e \u003cp\u003e10.6.2.2 Spectral Angle Mapper (SAM) 254\u003c\/p\u003e \u003cp\u003e10.6.2.3 Spectral Matched Vilter (SMF) 254\u003c\/p\u003e \u003cp\u003e10.6.2.4 Matched Subspace Detector (MSD) 255\u003c\/p\u003e \u003cp\u003e10.6.3 Hybrid Techniques 255\u003c\/p\u003e \u003cp\u003e10.7 Conclusions 256\u003c\/p\u003e \u003cp\u003eReferences 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 A Hybrid Approach for Band Selection of Hyperspectral Images 263\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAditi Roy Chowdhury, Joydev Hazra, and Paramartha Dutta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 263\u003c\/p\u003e \u003cp\u003e11.2 Relevant Concept Revisit 266\u003c\/p\u003e \u003cp\u003e11.2.1 Feature Extraction 266\u003c\/p\u003e \u003cp\u003e11.2.2 Feature Selection Using 2D PCA 266\u003c\/p\u003e \u003cp\u003e11.2.3 Immune Clonal System 267\u003c\/p\u003e \u003cp\u003e11.2.4 Fuzzy KNN 268\u003c\/p\u003e \u003cp\u003e11.3 Proposed Algorithm 271\u003c\/p\u003e \u003cp\u003e11.4 Experiment and Result 271\u003c\/p\u003e \u003cp\u003e11.4.1 Description of the Data Set 272\u003c\/p\u003e \u003cp\u003e11.4.2 Experimental Details 274\u003c\/p\u003e \u003cp\u003e11.4.3 Analysis of Results 275\u003c\/p\u003e \u003cp\u003e11.5 Conclusion 278\u003c\/p\u003e \u003cp\u003eReferences 279\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Uncertainty-Based Clustering Algorithms for Medical Image Analysis 283\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDeepthi P. Hudedagaddi and B.K. Tripathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 283\u003c\/p\u003e \u003cp\u003e12.2 Uncertainty-Based Clustering Algorithms 283\u003c\/p\u003e \u003cp\u003e12.2.1 Fuzzy C-Means 284\u003c\/p\u003e \u003cp\u003e12.2.2 Rough Fuzzy C-Means 285\u003c\/p\u003e \u003cp\u003e12.2.3 Intuitionistic Fuzzy C-Means 285\u003c\/p\u003e \u003cp\u003e12.2.4 Rough Intuitionistic Fuzzy C-Means 286\u003c\/p\u003e \u003cp\u003e12.3 Image Processing 286\u003c\/p\u003e \u003cp\u003e12.4 Medical Image Analysis with Uncertainty-Based Clustering Algorithms 287\u003c\/p\u003e \u003cp\u003e12.4.1 FCM with Spatial Information for Image Segmentation 287\u003c\/p\u003e \u003cp\u003e12.4.2 Fast and Robust FCM Incorporating Local Information for Image Segmentation 290\u003c\/p\u003e \u003cp\u003e12.4.3 Image Segmentation Using Spatial IFCM 291\u003c\/p\u003e \u003cp\u003e12.4.3.1 Applications of Spatial FCM and Spatial IFCM on Leukemia Images 292\u003c\/p\u003e \u003cp\u003e12.5 Conclusions 293\u003c\/p\u003e \u003cp\u003eReferences 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 An Optimized Breast Cancer Diagnosis SystemUsing a Cuckoo Search Algorithm and Support Vector Machine Classifier 297\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eManoharan Prabukumar, Loganathan Agilandeeswari, and Arun Kumar Sangaiah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 297\u003c\/p\u003e \u003cp\u003e13.2 Technical Background 301\u003c\/p\u003e \u003cp\u003e13.2.1 Morphological Segmentation 301\u003c\/p\u003e \u003cp\u003e13.2.2 Cuckoo Search Optimization Algorithm 302\u003c\/p\u003e \u003cp\u003e13.2.3 Support Vector Machines 303\u003c\/p\u003e \u003cp\u003e13.3 Proposed Breast Cancer Diagnosis System 303\u003c\/p\u003e \u003cp\u003e13.3.1 Preprocessing of Breast Cancer Image 303\u003c\/p\u003e \u003cp\u003e13.3.2 Feature Extraction 304\u003c\/p\u003e \u003cp\u003e13.3.2.1 Geometric Features 304\u003c\/p\u003e \u003cp\u003e13.3.2.2 Texture Features 305\u003c\/p\u003e \u003cp\u003e13.3.2.3 Statistical Features 306\u003c\/p\u003e \u003cp\u003e13.3.3 Features Selection 306\u003c\/p\u003e \u003cp\u003e13.3.4 Features Classification 307\u003c\/p\u003e \u003cp\u003e13.4 Results and Discussions 307\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 310\u003c\/p\u003e \u003cp\u003e13.6 FutureWork 310\u003c\/p\u003e \u003cp\u003eReferences 310\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Analysis of Hand Vein Images Using Hybrid Techniques 315\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eR. Sudhakar, S. Bharathi, and V. Gurunathan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 315\u003c\/p\u003e \u003cp\u003e14.2 Analysis of Vein Images in the Spatial Domain 318\u003c\/p\u003e \u003cp\u003e14.2.1 Preprocessing 318\u003c\/p\u003e \u003cp\u003e14.2.2 Feature Extraction 319\u003c\/p\u003e \u003cp\u003e14.2.3 Feature-Level Fusion 320\u003c\/p\u003e \u003cp\u003e14.2.4 Score Level Fusion 320\u003c\/p\u003e \u003cp\u003e14.2.5 Results and Discussion 322\u003c\/p\u003e \u003cp\u003e14.2.5.1 Evaluation Metrics 323\u003c\/p\u003e \u003cp\u003e14.3 Analysis of Vein Images in the Frequency Domain 326\u003c\/p\u003e \u003cp\u003e14.3.1 Preprocessing 326\u003c\/p\u003e \u003cp\u003e14.3.2 Feature Extraction 326\u003c\/p\u003e \u003cp\u003e14.3.3 Feature-Level Fusion 330\u003c\/p\u003e \u003cp\u003e14.3.4 Support Vector Machine Classifier 331\u003c\/p\u003e \u003cp\u003e14.3.5 Results and Discussion 331\u003c\/p\u003e \u003cp\u003e14.4 Comparative Analysis of Spatial and Frequency Domain Systems 332\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 335\u003c\/p\u003e \u003cp\u003eReferences 335\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Identification of Abnormal Masses in Digital Mammogram Using Statistical Decision Making 339\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIndra Kanta Maitra and Samir Kumar Bandyopadhyay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 339\u003c\/p\u003e \u003cp\u003e15.1.1 Breast Cancer 339\u003c\/p\u003e \u003cp\u003e15.1.2 Computer-Aided Detection\/Diagnosis (CAD) 340\u003c\/p\u003e \u003cp\u003e15.1.3 Segmentation 340\u003c\/p\u003e \u003cp\u003e15.2 PreviousWorks 341\u003c\/p\u003e \u003cp\u003e15.3 Proposed Method 343\u003c\/p\u003e \u003cp\u003e15.3.1 Preparation 343\u003c\/p\u003e \u003cp\u003e15.3.2 Preprocessing 345\u003c\/p\u003e \u003cp\u003e15.3.2.1 Image Enhancement and Edge Detection 346\u003c\/p\u003e \u003cp\u003e15.3.2.2 Isolation and Suppression of Pectoral Muscle 348\u003c\/p\u003e \u003cp\u003e15.3.2.3 Breast Contour Detection 351\u003c\/p\u003e \u003cp\u003e15.3.2.4 Anatomical Segmentation 353\u003c\/p\u003e \u003cp\u003e15.3.3 Identification of Abnormal Region(s) 354\u003c\/p\u003e \u003cp\u003e15.3.3.1 Coloring of Regions 354\u003c\/p\u003e \u003cp\u003e15.3.3.2 Statistical Decision Making 355\u003c\/p\u003e \u003cp\u003e15.4 Experimental Result 358\u003c\/p\u003e \u003cp\u003e15.4.1 Case Study with Normal Mammogram 358\u003c\/p\u003e \u003cp\u003e15.4.2 Case Study with Abnormalities Embedded in Fatty Tissues 358\u003c\/p\u003e \u003cp\u003e15.4.3 Case Study with Abnormalities Embedded in Fatty-Fibro-Glandular Tissues 359\u003c\/p\u003e \u003cp\u003e15.4.4 Case Study with Abnormalities Embedded in Dense-Fibro-Glandular Tissues 359\u003c\/p\u003e \u003cp\u003e15.5 Result Evaluation 360\u003c\/p\u003e \u003cp\u003e15.5.1 Statistical Analysis 361\u003c\/p\u003e \u003cp\u003e15.5.2 ROC Analysis 361\u003c\/p\u003e \u003cp\u003e15.5.3 Accuracy Estimation 365\u003c\/p\u003e \u003cp\u003e15.6 Comparative Analysis 366\u003c\/p\u003e \u003cp\u003e15.7 Conclusion 366\u003c\/p\u003e \u003cp\u003eAcknowledgments 366\u003c\/p\u003e \u003cp\u003eReferences 367\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Automatic Detection of Coronary Artery Stenosis Using Bayesian Classification and Gaussian Filters Based on Differential Evolution 369\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIvan Cruz-Aceves, Fernando Cervantes-Sanchez, and Arturo Hernandez-Aguirre\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 369\u003c\/p\u003e \u003cp\u003e16.2 Background 370\u003c\/p\u003e \u003cp\u003e16.2.1 Gaussian Matched Filters 371\u003c\/p\u003e \u003cp\u003e16.2.2 Differential Evolution 371\u003c\/p\u003e \u003cp\u003e16.2.2.1 Example: Global Optimization of the Ackley Function 373\u003c\/p\u003e \u003cp\u003e16.2.3 Bayesian Classification 375\u003c\/p\u003e \u003cp\u003e16.2.3.1 Example: Classification Problem 375\u003c\/p\u003e \u003cp\u003e16.3 Proposed Method 377\u003c\/p\u003e \u003cp\u003e16.3.1 Optimal Parameter Selection of GMF Using Differential Evolution 377\u003c\/p\u003e \u003cp\u003e16.3.2 Thresholding of the Gaussian Filter Response 378\u003c\/p\u003e \u003cp\u003e16.3.3 Stenosis Detection Using Second-Order Derivatives 378\u003c\/p\u003e \u003cp\u003e16.3.4 Stenosis Detection Using Bayesian Classification 379\u003c\/p\u003e \u003cp\u003e16.4 Computational Experiments 381\u003c\/p\u003e \u003cp\u003e16.4.1 Results of Vessel Detection 382\u003c\/p\u003e \u003cp\u003e16.4.2 Results of Vessel Segmentation 382\u003c\/p\u003e \u003cp\u003e16.4.3 Evaluation of Detection of Coronary Artery Stenosis 384\u003c\/p\u003e \u003cp\u003e16.5 Concluding Remarks 386\u003c\/p\u003e \u003cp\u003eAcknowledgment 388\u003c\/p\u003e \u003cp\u003eReferences 388\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Evaluating the Efficacy of Multi-resolution Texture Features for Prediction of Breast Density UsingMammographic Images 391\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKriti, Harleen Kaur, and Jitendra Virmani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 391\u003c\/p\u003e \u003cp\u003e17.1.1 Comparison of Related Methods with the Proposed Method 397\u003c\/p\u003e \u003cp\u003e17.2 Materials and Methods 398\u003c\/p\u003e \u003cp\u003e17.2.1 Description of Database 398\u003c\/p\u003e \u003cp\u003e17.2.2 ROI Extraction Protocol 398\u003c\/p\u003e \u003cp\u003e17.2.3 Workflow for CAD System Design 398\u003c\/p\u003e \u003cp\u003e17.2.3.1 Feature Extraction 400\u003c\/p\u003e \u003cp\u003e17.2.3.2 Classification 407\u003c\/p\u003e \u003cp\u003e17.3 Results 410\u003c\/p\u003e \u003cp\u003e17.3.1 Results Based on Classification Performance of the Classifiers (Classification Accuracy and Sensitivity) for Each Class 411\u003c\/p\u003e \u003cp\u003e17.3.1.1 Experiment I: To Determine the Performance of Different FDVs Using SVM Classifier 411\u003c\/p\u003e \u003cp\u003e17.3.1.2 Experiment II: To Determine the Performance of Different FDVs Using SSVM Classifier 412\u003c\/p\u003e \u003cp\u003e17.3.2 Results Based on Computational Efficiency of Classifiers for Predicting 161 Instances of Testing Dataset 412\u003c\/p\u003e \u003cp\u003e17.4 Conclusion and Future Scope 413\u003c\/p\u003e \u003cp\u003eReferences 415\u003c\/p\u003e \u003cp\u003eIndex 423\u003c\/p\u003e   \u003cp\u003e\u003cb\u003e PROF. (DR.) SIDDHARTHA BHATTACHARYYA\u003c\/b\u003e (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 \u0026amp; Development) of the institute. He is a co-author of 3 books and co-editor of 5 books and more than 135 research publications.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003e DR. INDRAJIT PAN\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003e DR. ANIRBAN MUKHERJEE\u003c\/b\u003e 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 \u0026amp; 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 \u0026amp; 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003e PROF. (DR.) PARAMARTHA DUTTA\u003c\/b\u003e 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.    \u003c\/p\u003e\u003cp\u003e\u003cb\u003e A synergy of techniques on hybrid intelligence for real-life image analysis \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003e Hybrid Intelligence for Image Analysis and Understanding\u003c\/i\u003e brings together research on the latest results and progress in the development of hybrid intelligent techniques for faithful image analysis and understanding. As such, the focus is on the methods of computational intelligence, with an emphasis on hybrid intelligent methods applied to image analysis and understanding.\u003c\/p\u003e \u003cp\u003eThe book offers a diverse range of hybrid intelligence techniques under the umbrellas of image thresholding, image segmentation, image analysis and video analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e Key features: \u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eProvides in-depth analysis of hybrid intelligent paradigms.\u003c\/li\u003e \u003cli\u003eProvides ample case studies, illustrations and photographs of real-life examples to illustrate findings and applications of different hybrid intelligent paradigms.\u003c\/li\u003e \u003cli\u003eOffers new solutions to recent problems in computer science, specifically in the application of hybrid intelligent techniques for image analysis and understanding, using well-known contemporary algorithms.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book is an essential reference for lecturers, researchers and graduate students in electrical engineering and computer science specializing in image processing or computational intelligence.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989392113893,"sku":"NP9781119242925","price":156.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119242925.jpg?v=1761783933","url":"https:\/\/k12savings.com\/es\/products\/hybrid-intelligence-for-image-analysis-and-understanding-isbn-9781119242925","provider":"K12savings","version":"1.0","type":"link"}