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Preface xi 1 Land-Use Classification with Integrated Data 1 D. A. Meedeniya, J. A. A. M Jayanetti, M. D. N. Dilini, M. H. Wickramapala and J. H. Madushanka 1.1 Introduction 2 1.2 Background Study 3 1.2.1 Overview of Land-Use and Land-Cover Information 3 1.2.2 Geographical Information Systems 4 1.2.3 GIS-Related Data Types 4 1.2.3.1 Point Data Sets 4 1.2.3.2 Aerial Data Sets 5 1.2.4 Related Studies 6 1.3 System Design 6 1.4 Implementation Details 10 1.4.1 Materials 10 1.4.2 Preprocessing 11 1.4.3 Built-Up Area Extraction 11 1.4.4 Per-Pixel Classification 12 1.4.5 Clustering 14 1.4.6 Segmentation 14 1.4.7 Object-Based Image Classification 16 1.4.8 Foursquare Data Preprocessing and Quality Analysis 20 1.4.9 Integration of Satellite Images with Foursquare Data 21 1.4.10 Building Block Identification 21 1.4.11 Overlay of Foursquare Points 22 1.4.12 Visualization of Land Usage 23 1.4.13 Common Platform Development 23 1.5 System Evaluation 25 1.5.1 Experimental Evaluation Process 25 1.5.2 Evaluation of the Classification Using Base Error Matrix 28 1.6 Discussion 31 1.6.1 Contribution of the Proposed Approach 31 1.6.2 Limitations of the Data Sets 32 1.6.3 Future Research Directions 33 1.7 Conclusion 34 References 35 2 Indian Sign Language Recognition Using Soft Computing Techniques 37 Ashok Kumar Sahoo, Pradeepta Kumar Sarangi and Parul Goyal 2.1 Introduction 37 2.2 Related Works 38 2.2.1 The Domain of Sign Language 39 2.2.2 The Data Acquisition Methods 41 2.2.3 Preprocessing Steps 42 2.2.3.1 Image Restructuring 43 2.2.3.2 Skin Color Detection 43 2.2.4 Methods of Feature Extraction Used in the Experiments 44 2.2.5 Classification Techniques 45 2.2.5.1 K-Nearest Neighbor 45 2.2.5.2 Neural Network Classifier 45 2.2.5.3 Naive Bayes Classifier 46 2.3 Experiments 46 2.3.1 Experiments on ISL Digits 46 2.3.1.1 Results and Discussions on the First Experiment 47 2.3.1.2 Results and Discussions on Second Experiment 49 2.3.2 Experiments on ISL Alphabets 51 2.3.2.1 Experiments with Single-Handed Alphabet Signs 51 2.3.2.2 Results of Single-Handed Alphabet Signs 52 2.3.2.3 Experiments with Double-Handed Alphabet Signs 53 2.3.2.4 Results on Double-Handed Alphabets 54 2.3.3 Experiments on ISL Words 58 2.3.3.1 Results on ISL Word Signs 59 2.4 Summary 63 References 63 3 Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model 67 Kalyan Kumar Jena, Sasmita Mishra, Sarojananda Mishra and Sourav Kumar Bhoi 3.1 Introduction 68 3.2 Related Work 69 3.3 Proposed Model 70 3.4 Results and Discussion 72 3.5 Conclusion 77 References 78 4 Object Descriptor for Machine Vision 85 Aparna S. Murthy and Salah Rabba 4.1 Outline 85 4.2 Chain Codes 87 4.3 Polygonal Approximation 89 4.4 Moments 92 4.5 HU Invariant Moments 96 4.6 Zernike Moments 97 4.7 Fourier Descriptors 98 4.8 Quadtree 99 4.9 Conclusion 102 References 114 5 Flood Disaster Management: Risks, Technologies, and Future Directions 115 Hafiz Suliman Munawar 5.1 Flood Management 115 5.1.1 Introduction 115 5.1.2 Global Flood Risks and Incidents 116 5.1.3 Causes of Floods 118 5.1.4 Floods in Pakistan 119 5.1.5 Floods in Australia 121 5.1.6 Why Floods are a Major Concern 123 5.2 Existing Disaster Management Systems 124 5.2.1 Introduction 124 5.2.2 Disaster Management Systems Used Around the World 124 5.2.2.1 Disaster Management Model 125 5.2.2.2 Disaster Risk Analysis System 126 5.2.2.3 Geographic Information System 126 5.2.2.4 Web GIS 126 5.2.2.5 Remote Sensing 127 5.2.2.6 Satellite Imaging 127 5.2.2.7 Global Positioning System for Imaging 128 5.2.3 Gaps in Current Disaster Management Technology 128 5.3 Advancements in Disaster Management Technologies 129 5.3.1 Introduction 129 5.3.2 AI and Machine Learning for Disaster Management 130 5.3.2.1 AIDR 130 5.3.2.2 Warning Systems 130 5.3.2.3 QCRI 131 5.3.2.4 The Concern 131 5.3.2.5 BlueLine Grid 131 5.3.2.6 Google Maps 132 5.3.2.7 RADARSAT-1 132 5.3.3 Recent Research in Disaster Management 132 5.3.4 Conclusion 137 5.4 Proposed System 137 5.4.1 Image Acquisition Through UAV 138 5.4.2 Preprocessing 138 5.4.3 Landmarks Detection 138 5.4.3.1 Buildings 139 5.4.3.2 Roads 139 5.4.4 Flood Detection 140 5.4.4.1 Feature Matching 140 5.4.4.2 Flood Detection Using Machine Learning 141 5.4.5 Conclusion 143 References 143 6 Temporal Color Analysis of Avocado Dip for Quality Control 147 Homero V. Rios-Figueroa, Micloth Lopez del Castillo-Lozano, Elvia K. Ramirez-Gomez and Ericka J. Rechy-Ramirez 6.1 Introduction 147 6.2 Materials and Methods 148 6.3 Image Acquisition 149 6.4 Image Processing 150 6.5 Experimental Design 150 6.5.1 First Experimental Design 150 6.5.2 Second Experimental Design 151 6.6 Results and Discussion 151 6.6.1 First Experimental Design (RGB Color Space) 151 6.6.2 Second Experimental Design (L*a*b* Color Space) 152 6.7 Conclusion 156 References 156 7 Image and Video Processing for Defect Detection in Key Infrastructure 159 Hafiz Suliman Munawar 7.1 Introduction 160 7.2 Reasons for Defective Roads and Bridges 161 7.3 Image Processing for Defect Detection 162 7.3.1 Feature Extraction 162 7.3.2 Morphological Operators 163 7.3.3 Cracks Detection 164 7.3.4 Potholes Detection 165 7.3.5 Water Puddles Detection 166 7.3.6 Pavement Distress Detection 167 7.4 Image-Based Defect Detection Methods 169 7.4.1 Thresholding Techniques 170 7.4.2 Edge Detection Techniques 170 7.4.3 Wavelet Transform Techniques 171 7.4.4 Texture Analysis Techniques 171 7.4.5 Machine Learning Techniques 172 7.5 Factors Affecting the Performance 172 7.5.1 Lighting Variations 173 7.5.2 Small Database 173 7.5.3 Low-Quality Data 173 7.6 Achievements and Issues 173 7.6.1 Achievements 174 7.6.2 Issues 174 7.7 Conclusion 174 References 175 8 Methodology for the Detection of Asymptomatic Diabetic Retinopathy 179 Jaskirat Kaur and Deepti Mittal 8.1 Introduction 180 8.2 Key Steps of Computer-Aided Diagnostic Methods 181 8.3 DR Screening and Grading Methods 183 8.4 Key Observations from Literature Review 188 8.5 Design of Experimental Methodology 189 8.6 Conclusion 192 References 193 9 Offline Handwritten Numeral Recognition Using Convolution Neural Network 197 Abhisek Sethy, Prashanta Kumar Patra and Soumya Ranjan Nayak 9.1 Introduction 198 9.2 Related Work Done 199 9.3 Data Set Used for Simulation 201 9.4 Proposed Model 202 9.5 Result Analysis 204 9.6 Conclusion and Future Work 207 References 209 10 A Review on Phishing-Machine Vision and Learning Approaches 213 Hemamalini Siranjeevi, Swaminathan Venkatraman and Kannan Krithivasan 10.1 Introduction 213 10.2 Literature Survey 214 10.2.1 Content-Based Approaches 214 10.2.2 Heuristics-Based Approaches 215 10.2.3 Blacklist-Based Approaches 215 10.2.4 Whitelist-Based Approaches 216 10.2.5 CANTINA-Based Approaches 216 10.2.6 Image-Based Approaches 216 10.3 Role of Data Mining in Antiphishing 217 10.3.1 Phishing Detection 219 10.3.2 Phishing Prevention 220 10.3.3 Training and Education 222 10.3.4 Phishing Recovery and Avoidance 222 10.3.5 Visual Methods 223 10.4 Conclusion 224 Acknowledgments 224 References 224 Index 231

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