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 © 2021 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. 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Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. 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