Machine Vision Inspection Systems, Machine Learning-Based Approaches

337 Pages·2021·39.869 MB·English

by  Muthukumaran Malarvel, Soumya Ranjan Nayak, Prasant Kumar Pattnaik, Surya Narayan Panda

Machine Vision Inspection
Systems, Volume 2
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected]) Machine Vision Inspection
Phillip Carmical ([email protected])
Systems, Volume 2
Machine Learning-Based Approaches
Edited by
Muthukumaran Malarvel
Chitkara University Institute of Engineering and Technology,
Chitkara University, Punjab, India
Soumya Ranjan Nayak
Amity School of Engineering and Technology, Amity University
Uttar Pradesh, Noida, India
Prasant Kumar Pattnaik
School of Computer Engineering, KIIT Deemed to be University, India
and
Surya Narayan Panda
Chitkara University Institute of Engineering and Technology,
Chitkara University, Punjab, India
This edition first published 2021 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
ISBN 978-1-119-78609-2
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 xiii
1 Machine Learning-Based Virus Type Classification Using
Transmission Electron Microscopy Virus Images 1
Kalyan Kumar Jena, Sourav Kumar Bhoi,
Soumya Ranjan Nayak and Chittaranjan Mallick
1.1 Introduction 2
1.2 Related Works 3
1.3 Methodology 4
1.4 Results and Discussion 6
1.5 Conclusion 16
References 16
2 Capsule Networks for Character Recognition in
Low Resource Languages 23
C. Abeysinghe, I. Perera and D.A. Meedeniya
2.1 Introduction 24
2.2 Background Study 25
2.2.1 Convolutional Neural Networks 25
2.2.2 Related Studies on One-Shot Learning 26
2.2.3 Character Recognition as a One-Shot Task 26
2.3 System Design 28
2.3.1 One-Shot Learning Implementation 31
2.3.2 Optimization and Learning 31
2.3.3 Dataset 32
2.3.4 Training Process 32
2.4 Experiments and Results 33
2.4.1 N-Way Classification 34
2.4.2 Within Language Classification 37
2.4.3 MNIST Classification 39
v
vi Contents
2.4.4 Sinhala Language Classification 41
2.5 Discussion 41
2.5.1 Study Contributions 41
2.5.2 Challenges and Future Research Directions 42
2.5.3 Conclusion 43
References 43
3 An Innovative Extended Method of Optical Pattern
Recognition for Medical Images With Firm Accuracy—
4f System-Based Medical Optical Pattern Recognition 47
Dhivya Priya E.L., D. Jeyabharathi, K.S. Lavanya,
S. Thenmozhi, R. Udaiyakumar and A. Sharmila
3.1 Introduction 48
3.1.1 Fourier Optics 48
3.2 Optical Signal Processing 50
3.2.1 Diffraction of Light 50
3.2.2 Biconvex Lens 51
3.2.3 4f System 51
3.2.4 Literature Survey 52
3.3 Extended Medical Optical Pattern Recognition 55
3.3.1 Optical Fourier Transform 55
3.3.2 Fourier Transform Using a Lens 55
3.3.3 Fourier Transform in the Far Field 56
3.3.4 Correlator Signal Processing 56
3.3.5 Image Formation in 4f System 57
3.3.6 Extended Medical Optical Pattern Recognition 58
3.4 Initial 4f System 59
3.4.1 Extended 4f System 59
3.4.2 Setup of 45 Degree 59
3.4.3 Database Creation 59
3.4.4 Superimposition of Diffracted Pattern 60
3.4.5 Image Plane 60
3.5 Simulation Output 60
3.5.1 MATLAB 60
3.5.2 Sample Input Images 61
3.5.3 Output Simulation 61
3.6 Complications in Real Time Implementation 64
3.6.1 Database Creation 64
3.6.2 Accuracy 65
3.6.3 Optical Setup 65
Contents vii
3.7 Future Enhancements 65
References 65
4 Brain Tumor Diagnostic System— A Deep Learning Application 69
Kalaiselvi, T. and Padmapriya, S.T.
4.1 Introduction 69
4.1.1 Intelligent Systems 69
4.1.2 Applied Mathematics in Machine Learning 70
4.1.3 Machine Learning Basics 72
4.1.4 Machine Learning Algorithms 73
4.2 Deep Learning 75
4.2.1 Evolution of Deep Learning 75
4.2.2 Deep Networks 76
4.2.3 Convolutional Neural Networks 77
4.3 Brain Tumor Diagnostic System 80
4.3.1 Brain Tumor 80
4.3.2 Methodology 80
4.3.3 Materials and Metrics 84
4.3.4 Results and Discussions 85
4.4 Computer-Aided Diagnostic Tool 86
4.5 Conclusion and Future Enhancements 87
References 88
5 Machine Learning for Optical Character Recognition System 91
Gurwinder Kaur and Tanya Garg
5.1 Introduction 91
5.2 Character Recognition Methods 92
5.3 Phases of Recognition System 93
5.3.1 Image Acquisition 93
5.3.2 Defining ROI 94
5.3.3 Pre-Processing 94
5.3.4 Character Segmentation 94
5.3.5 Skew Detection and Correction 95
5.3.6 Binarization 95
5.3.7 Noise Removal 97
5.3.8 Thinning 97
5.3.9 Representation 97
5.3.10 Feature Extraction 98
5.3.11 Training and Recognition 98
5.4 Post-Processing 101
viii Contents
5.5 Performance Evaluation 103
5.5.1 Recognition Rate 103
5.5.2 Rejection Rate 103
5.5.3 Error Rate 103
5.6 Applications of OCR Systems 104
5.7 Conclusion and Future Scope 105
References 105
6 Surface Defect Detection Using SVM-Based Machine Vision
System with Optimized Feature 109
Ashok Kumar Patel, Venkata Naresh Mandhala,
Dinesh Kumar Anguraj and Soumya Ranjan Nayak
6.1 Introduction 110
6.2 Methodology 113
6.2.1 Data Collection 113
6.2.2 Data Pre-Processing 113
6.2.3 Feature Extraction 115
6.2.4 Feature Optimization 116
6.2.5 Model Development 119
6.2.6 Performance Evaluation 120
6.3 Conclusion 123
References 124
7 Computational Linguistics-Based Tamil Character
Recognition System for Text to Speech Conversion 129
Suriya, S., Balaji, M., Gowtham, T.M. and Rahul, Kumar S.
7.1 Introduction 130
7.2 Literature Survey 130
7.3 Proposed Approach 134
7.4 Design and Analysis 134
7.5 Experimental Setup and Implementation 136
7.6 Conclusion 151
References 151
8 A Comparative Study of Different Classifiers to Propose
a GONN for Breast Cancer Detection 155
Ankita Tiwari, Bhawana Sahu, Jagalingam Pushaparaj
and Muthukumaran Malarvel
8.1 Introduction 156
8.2 Methodology 157
8.2.1 Dataset 157
Contents ix
8.2.2 Linear Regression 159
8.2.2.1 Correlation 160
8.2.2.2 Covariance 160
8.2.3 Classification Algorithm 161
8.2.3.1 Support Vector Machine 161
8.2.3.2 Random Forest Classifier 162
8.2.3.3 K-Nearest Neighbor Classifier 163
8.2.3.4 Decision Tree Classifier 163
8.2.3.5 Multi-Layered Perceptron 164
8.3 Results and Discussion 165
8.4 Conclusion 169
References 169
9 Mexican Sign-Language Static-Alphabet Recognition
Using 3D Affine Invariants 171
Guadalupe Carmona-Arroyo, Homero V. Rios-Figueroa
and Martha Lorena Avendaño-Garrido
9.1 Introduction 171
9.2 Pattern Recognition 175
9.2.1 3D Affine Invariants 175
9.3 Experiments 177
9.3.1 Participants 179
9.3.2 Data Acquisition 179
9.3.3 Data Augmentation 179
9.3.4 Feature Extraction 181
9.3.5 Classification 181
9.4 Results 182
9.4.1 Experiment 1 182
9.4.2 Experiment 2 184
9.4.3 Experiment 3 184
9.5 Discussion 188
9.6 Conclusion 189
Acknowledgments 190
References 190
10 Performance of Stepped Bar Plate-Coated Nanolayer
of a Box Solar Cooker Control Based on Adaptive
Tree Traversal Energy and OSELM 193
S. Shanmugan, F.A. Essa, J. Nagaraj and Shilpa Itnal
10.1 Introduction 194