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

Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Land-Use Classification with Integrated Data -- 1.1 Introduction -- 1.2 Background Study -- 1.2.1 Overview of Land-Use and Land-Cover Information -- 1.2.2 Geographical Information Systems -- 1.2.3 GIS-Related Data Types -- 1.2.3.1 Point Data Sets -- 1.2.3.2 Aerial Data Sets -- 1.2.4 Related Studies -- 1.3 System Design -- 1.4 Implementation Details -- 1.4.1 Materials -- 1.4.2 Preprocessing -- 1.4.3 Built-Up Area Extraction -- 1.4.4 Per-Pixel Classification -- 1.4.5 Clustering -- 1.4.6 Segmentation -- 1.4.7 Object-Based Image Classification -- 1.4.8 Foursquare Data Preprocessing and Quality Analysis -- 1.4.9 Integration of Satellite Images with Foursquare Data -- 1.4.10 Building Block Identification -- 1.4.11 Overlay of Foursquare Points -- 1.4.12 Visualization of Land Usage -- 1.4.13 Common Platform Development -- 1.5 System Evaluation -- 1.5.1 Experimental Evaluation Process -- 1.5.2 Evaluation of the Classification Using Base Error Matrix -- 1.6 Discussion -- 1.6.1 Contribution of the Proposed Approach -- 1.6.2 Limitations of the Data Sets -- 1.6.3 Future Research Directions -- 1.7 Conclusion -- References -- Chapter 2 Indian Sign Language Recognition Using Soft Computing Techniques -- 2.1 Introduction -- 2.2 Related Works -- 2.2.1 The Domain of Sign Language -- 2.2.2 The Data Acquisition Methods -- 2.2.3 Preprocessing Steps -- 2.2.3.1 Image Restructuring -- 2.2.3.2 Skin Color Detection -- 2.2.4 Methods of Feature Extraction Used in the Experiments -- 2.2.5 Classification Techniques -- 2.2.5.1 K-Nearest Neighbor -- 2.2.5.2 Neural Network Classifier -- 2.2.5.3 Naive Baÿes Classifier -- 2.3 Experiments -- 2.3.1 Experiments on ISL Digits -- 2.3.1.1 Results and Discussions on the First Experiment -- 2.3.1.2 Results and Discussions on Second Experiment.

2.3.2 Experiments on ISL Alphabets -- 2.3.2.1 Experiments with Single-Handed Alphabet Signs -- 2.3.2.2 Results of Single-Handed Alphabet Signs -- 2.3.2.3 Experiments with Double-Handed Alphabet Signs -- 2.3.2.4 Results on Double-Handed Alphabets -- 2.3.3 Experiments on ISL Words -- 2.3.3.1 Results on ISL Word Signs -- 2.4 Summary -- References -- Chapter 3 Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Proposed Model -- 3.4 Results and Discussion -- 3.5 Conclusion -- References -- Chapter 4 Object Descriptor for Machine Vision -- 4.1 Outline -- 4.2 Chain Codes -- 4.3 Polygonal Approximation -- 4.4 Moments -- 4.5 HU Invariant Moments -- 4.6 Zernike Moments -- 4.7 Fourier Descriptors -- 4.8 Quadtree -- 4.9 Conclusion -- References -- Chapter 5 Flood Disaster Management: Risks, Technologies, and Future Directions -- 5.1 Flood Management -- 5.1.1 Introduction -- 5.1.2 Global Flood Risks and Incidents -- 5.1.3 Causes of Floods -- 5.1.4 Floods in Pakistan -- 5.1.5 Floods in Australia -- 5.1.6 Why Floods are a Major Concern -- 5.2 Existing Disaster Management Systems -- 5.2.1 Introduction -- 5.2.2 Disaster Management Systems Used Around the World -- 5.2.2.1 Disaster Management Model -- 5.2.2.2 Disaster Risk Analysis System -- 5.2.2.3 Geographic Information System -- 5.2.2.4 Web GIS -- 5.2.2.5 Remote Sensing -- 5.2.2.6 Satellite Imaging -- 5.2.2.7 Global Positioning System for Imaging -- 5.2.3 Gaps in Current Disaster Management Technology -- 5.3 Advancements in Disaster Management Technologies -- 5.3.1 Introduction -- 5.3.2 AI and Machine Learning for Disaster Management -- 5.3.2.1 AIDR -- 5.3.2.2 Warning Systems -- 5.3.2.3 QCRI -- 5.3.2.4 The Concern -- 5.3.2.5 BlueLine Grid -- 5.3.2.6 Google Maps -- 5.3.2.7 RADARSAT-1.

5.3.3 Recent Research in Disaster Management -- 5.3.4 Conclusion -- 5.4 Proposed System -- 5.4.1 Image Acquisition Through UAV -- 5.4.2 Preprocessing -- 5.4.3 Landmarks Detection -- 5.4.3.1 Buildings -- 5.4.3.2 Roads -- 5.4.4 Flood Detection -- 5.4.4.1 Feature Matching -- 5.4.4.2 Flood Detection Using Machine Learning -- 5.4.5 Conclusion -- References -- Chapter 6 Temporal Color Analysis of Avocado Dip for Quality Control -- 6.1 Introduction -- 6.2 Materials and Methods -- 6.3 Image Acquisition -- 6.4 Image Processing -- 6.5 Experimental Design -- 6.5.1 First Experimental Design -- 6.5.2 Second Experimental Design -- 6.6 Results and Discussion -- 6.6.1 First Experimental Design (RGB Color Space) -- 6.6.2 Second Experimental Design (L*a*b* Color Space) -- 6.7 Conclusion -- References -- Chapter 7 Image and Video Processing for Defect Detection in Key Infrastructure -- 7.1 Introduction -- 7.2 Reasons for Defective Roads and Bridges -- 7.3 Image Processing for Defect Detection -- 7.3.1 Feature Extraction -- 7.3.2 Morphological Operators -- 7.3.3 Cracks Detection -- 7.3.4 Potholes Detection -- 7.3.5 Water Puddles Detection -- 7.3.6 Pavement Distress Detection -- 7.4 Image-Based Defect Detection Methods -- 7.4.1 Thresholding Techniques -- 7.4.2 Edge Detection Techniques -- 7.4.3 Wavelet Transform Techniques -- 7.4.4 Texture Analysis Techniques -- 7.4.5 Machine Learning Techniques -- 7.5 Factors Affecting the Performance -- 7.5.1 Lighting Variations -- 7.5.2 Small Database -- 7.5.3 Low-Quality Data -- 7.6 Achievements and Issues -- 7.6.1 Achievements -- 7.6.2 Issues -- 7.7 Conclusion -- References -- Chapter 8 Methodology for the Detection of Asymptomatic Diabetic Retinopathy -- 8.1 Introduction -- 8.2 Key Steps of Computer-Aided Diagnostic Methods -- 8.3 DR Screening and Grading Methods -- 8.4 Key Observations from Literature Review.

8.5 Design of Experimental Methodology -- 8.6 Conclusion -- References -- Chapter 9 Offline Handwritten Numeral Recognition Using Convolution Neural Network -- 9.1 Introduction -- 9.2 Related Work Done -- 9.3 Data Set Used for Simulation -- 9.4 Proposed Model -- 9.5 Result Analysis -- 9.6 Conclusion and Future Work -- References -- Chapter 10 A Review on Phishing-Machine Vision and Learning Approaches -- 10.1 Introduction -- 10.2 Literature Survey -- 10.2.1 Content-Based Approaches -- 10.2.2 Heuristics-Based Approaches -- 10.2.3 Blacklist-Based Approaches -- 10.2.4 Whitelist-Based Approaches -- 10.2.5 CANTINA-Based Approaches -- 10.2.6 Image-Based Approaches -- 10.3 Role of Data Mining in Antiphishing -- 10.3.1 Phishing Detection -- 10.3.2 Phishing Prevention -- 10.3.3 Training and Education -- 10.3.4 Phishing Recovery and Avoidance -- 10.3.5 Visual Methods -- 10.4 Conclusion -- Acknowledgments -- References -- Index -- EULA.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.