Machine Vision Inspection Systems, Image Processing, Concepts, Methodologies, and Applications

Muthukumaran Malarvel obtained his PhD in Digital Image Processing and he is currently working as an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Punjab, India. His research interests include digital image processing, machine vision systems, image statistical analysis & feature extraction, and machine learning algorithms. Soumya Ranjan Nayak obtained his PhD in computer science and engineering from the Biju Patnaik University of Technology, India. He has more than a decade of teaching and research experience and currently is working as an Assistant Professor, Amity University, Noida, India. His research interests include image analysis on fractal geometry, color and texture analysis jointly and separately. Surya Narayan Panda is a Professor and Director Research at Chitkara University, Punjab, India. His areas of interest include Cybersecurity, Networking, Advanced Computer Networks, Machine Learning, and Artificial Intelligence. He has developed the prototype of Smart Portable Intensive Care Unit through which the doctor can provide immediate virtual medical assistance to emergency cases in the ambulance. He is currently involved in designing different healthcare devices for real-time issues using AI and ML. Prasant Kumar Pattnaik Ph.D. (Computer Science), Fellow IETE, Senior Member IEEE is a Professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He has more than a decade of teaching and research experience. His areas of interest include Mobile Computing, Cloud Computing, Cyber Security, Intelligent Systems and Brain Computer Interface. Nittaya Muangnak is a lecturer at Kasetsart University, Thailand. Her PhD research has been on medical image analysis, particularly retinal fundus image, at Sirindhorn International Institute of Technology, Thammasat University in Thailand.

• Preface xi1 Land-Use Classification with Integrated Data 1D. A. Meedeniya, J. A. A. M Jayanetti, M. D. N. Dilini, M. H. Wickramapala and J. H. Madushanka1.1 Introduction 21.2 Background Study 31.2.1 Overview of Land-Use and Land-Cover Information 31.2.2 Geographical Information Systems 41.2.3 GIS-Related Data Types 41.2.3.1 Point Data Sets 41.2.3.2 Aerial Data Sets 51.2.4 Related Studies 61.3 System Design 61.4 Implementation Details 101.4.1 Materials 101.4.2 Preprocessing 111.4.3 Built-Up Area Extraction 111.4.4 Per-Pixel Classification 121.4.5 Clustering 141.4.6 Segmentation 141.4.7 Object-Based Image Classification 161.4.8 Foursquare Data Preprocessing and Quality Analysis 201.4.9 Integration of Satellite Images with Foursquare Data 211.4.10 Building Block Identification 211.4.11 Overlay of Foursquare Points 221.4.12 Visualization of Land Usage 231.4.13 Common Platform Development 231.5 System Evaluation 251.5.1 Experimental Evaluation Process 251.5.2 Evaluation of the Classification Using Base Error Matrix 281.6 Discussion 311.6.1 Contribution of the Proposed Approach 311.6.2 Limitations of the Data Sets 321.6.3 Future Research Directions 331.7 Conclusion 34References 352 Indian Sign Language Recognition Using Soft Computing Techniques 37Ashok Kumar Sahoo, Pradeepta Kumar Sarangi and Parul Goyal2.1 Introduction 372.2 Related Works 382.2.1 The Domain of Sign Language 392.2.2 The Data Acquisition Methods 412.2.3 Preprocessing Steps 422.2.3.1 Image Restructuring 432.2.3.2 Skin Color Detection 432.2.4 Methods of Feature Extraction Used in the Experiments 442.2.5 Classification Techniques 452.2.5.1 K-Nearest Neighbor 452.2.5.2 Neural Network Classifier 452.2.5.3 Naive Baÿes Classifier 462.3 Experiments 462.3.1 Experiments on ISL Digits 462.3.1.1 Results and Discussions on the First Experiment 472.3.1.2 Results and Discussions on Second Experiment 492.3.2 Experiments on ISL Alphabets 512.3.2.1 Experiments with Single-Handed Alphabet Signs 512.3.2.2 Results of Single-Handed Alphabet Signs 522.3.2.3 Experiments with Double-Handed Alphabet Signs 532.3.2.4 Results on Double-Handed Alphabets 542.3.3 Experiments on ISL Words 582.3.3.1 Results on ISL Word Signs 592.4 Summary 63References 633 Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model 67Kalyan Kumar Jena, Sasmita Mishra, Sarojananda Mishra and Sourav Kumar Bhoi3.1 Introduction 683.2 Related Work 693.3 Proposed Model 703.4 Results and Discussion 723.5 Conclusion 77References 784 Object Descriptor for Machine Vision 85Aparna S. Murthy and Salah Rabba4.1 Outline 854.2 Chain Codes 874.3 Polygonal Approximation 894.4 Moments 924.5 HU Invariant Moments 964.6 Zernike Moments 974.7 Fourier Descriptors 984.8 Quadtree 994.9 Conclusion 102References 1145 Flood Disaster Management: Risks, Technologies, and Future Directions 115Hafiz Suliman Munawar5.1 Flood Management 1155.1.1 Introduction 1155.1.2 Global Flood Risks and Incidents 1165.1.3 Causes of Floods 1185.1.4 Floods in Pakistan 1195.1.5 Floods in Australia 1215.1.6 Why Floods are a Major Concern 1235.2 Existing Disaster Management Systems 1245.2.1 Introduction 1245.2.2 Disaster Management Systems Used Around the World 1245.2.2.1 Disaster Management Model 1255.2.2.2 Disaster Risk Analysis System 1265.2.2.3 Geographic Information System 1265.2.2.4 Web GIS 1265.2.2.5 Remote Sensing 1275.2.2.6 Satellite Imaging 1275.2.2.7 Global Positioning System for Imaging 1285.2.3 Gaps in Current Disaster Management Technology 1285.3 Advancements in Disaster Management Technologies 1295.3.1 Introduction 1295.3.2 AI and Machine Learning for Disaster Management 1305.3.2.1 AIDR 1305.3.2.2 Warning Systems 1305.3.2.3 QCRI 1315.3.2.4 The Concern 1315.3.2.5 BlueLine Grid 1315.3.2.6 Google Maps 1325.3.2.7 RADARSAT-1 1325.3.3 Recent Research in Disaster Management 1325.3.4 Conclusion 1375.4 Proposed System 1375.4.1 Image Acquisition Through UAV 1385.4.2 Preprocessing 1385.4.3 Landmarks Detection 1385.4.3.1 Buildings 1395.4.3.2 Roads 1395.4.4 Flood Detection 1405.4.4.1 Feature Matching 1405.4.4.2 Flood Detection Using Machine Learning 1415.4.5 Conclusion 143References 1436 Temporal Color Analysis of Avocado Dip for Quality Control 147Homero V. Rios-Figueroa, Micloth López del Castillo-Lozano, Elvia K. Ramirez-Gomez and Ericka J. Rechy-Ramirez6.1 Introduction 1476.2 Materials and Methods 1486.3 Image Acquisition 1496.4 Image Processing 1506.5 Experimental Design 1506.5.1 First Experimental Design 1506.5.2 Second Experimental Design 1516.6 Results and Discussion 1516.6.1 First Experimental Design (RGB Color Space) 1516.6.2 Second Experimental Design (L*a*b* Color Space) 1526.7 Conclusion 156References 1567 Image and Video Processing for Defect Detection in Key Infrastructure 159Hafiz Suliman Munawar7.1 Introduction 1607.2 Reasons for Defective Roads and Bridges 1617.3 Image Processing for Defect Detection 1627.3.1 Feature Extraction 1627.3.2 Morphological Operators 1637.3.3 Cracks Detection 1647.3.4 Potholes Detection 1657.3.5 Water Puddles Detection 1667.3.6 Pavement Distress Detection 1677.4 Image-Based Defect Detection Methods 1697.4.1 Thresholding Techniques 1707.4.2 Edge Detection Techniques 1707.4.3 Wavelet Transform Techniques 1717.4.4 Texture Analysis Techniques 1717.4.5 Machine Learning Techniques 1727.5 Factors Affecting the Performance 1727.5.1 Lighting Variations 1737.5.2 Small Database 1737.5.3 Low-Quality Data 1737.6 Achievements and Issues 1737.6.1 Achievements 1747.6.2 Issues 1747.7 Conclusion 174References 1758 Methodology for the Detection of Asymptomatic Diabetic Retinopathy 179Jaskirat Kaur and Deepti Mittal8.1 Introduction 1808.2 Key Steps of Computer-Aided Diagnostic Methods 1818.3 DR Screening and Grading Methods 1838.4 Key Observations from Literature Review 1888.5 Design of Experimental Methodology 1898.6 Conclusion 192References 1939 Offline Handwritten Numeral Recognition Using Convolution Neural Network 197Abhisek Sethy, Prashanta Kumar Patra and Soumya Ranjan Nayak9.1 Introduction 1989.2 Related Work Done 1999.3 Data Set Used for Simulation 2019.4 Proposed Model 2029.5 Result Analysis 2049.6 Conclusion and Future Work 207References 20910 A Review on Phishing—Machine Vision and Learning Approaches 213Hemamalini Siranjeevi, Swaminathan Venkatraman and Kannan Krithivasan10.1 Introduction 21310.2 Literature Survey 21410.2.1 Content-Based Approaches 21410.2.2 Heuristics-Based Approaches 21510.2.3 Blacklist-Based Approaches 21510.2.4 Whitelist-Based Approaches 21610.2.5 CANTINA-Based Approaches 21610.2.6 Image-Based Approaches 21610.3 Role of Data Mining in Antiphishing 21710.3.1 Phishing Detection 21910.3.2 Phishing Prevention 22010.3.3 Training and Education 22210.3.4 Phishing Recovery and Avoidance 22210.3.5 Visual Methods 22310.4 Conclusion 224Acknowledgments 224References 224Index 231