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

242 Pages·2021·27.937 MB·English

by  Muthukumaran Malarvel (editor), Soumya Ranjan Nayak (editor), Surya Narayan Panda (editor), Prasant Kumar Pattnaik (editor), Nittaya Muangnak (editor)

Machine Vision
Inspection Systems
Volume 1
Scrivener Publishing
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Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([e-mail])
Phillip Carmical ([e-mail])
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([e-mail]) Machine Vision
Phillip Carmical ([e-mail])
Inspection Systems
Volume 1
Image Processing, Concepts,
Methodologies and Applications
Edited by
Muthukumaran Malarvel,
Soumya Ranjan Nayak,
Surya Narayan Panda,
Prasant Kumar Pattnaik
and Nittaya Muangnak
This edition first published 2020 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
Names: Malarvel, Muthukumaran, editor. | Nayak, Soumya Ranjan, 1984–
editor. | Panda, Sury Narayan, editor. | Pattnaik, Prasant Kumar, 1969–
editor. | Muangnak, Nittaya, editor.
Title: Machine vision inspection systems / edited by Muthukumaran Malarvel,
Soumya Ranjan Nayak, Sury Narayan Panda, Prasant Kumar Pattnaik and
Nittaya Muangnak.
Description: Hoboken, NJ : Wiley-Scrivener, 2020. | Includes
bibliographical references and index. | Contents: Volume 1. Image
processing, concepts, methodologies and applications –
Identifiers: LCCN 2020020076 (print) | LCCN 2020020077 (ebook) | ISBN
9781119681809 (hardback) | ISBN 9781119681960 (adobe pdf) | ISBN
9781119682097 (epub)
Subjects: LCSH: Computer vision. | Computer vision–Industrial
applications. | Engineering inspection–Automation. | Image processing.
| Image processing–Digital techniques.
Classification: LCC TA1634 .M3354 2020 (print) | LCC TA1634 (ebook) | DDC
006.3/7–dc23
LC record available at https://lccn.loc.gov/2020020076
LC ebook record available at https://lccn.loc.gov/2020020077
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
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
v
vi Contents
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 Baÿes 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
Contents vii
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
viii Contents
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 López 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
Contents ix
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