Machine Vision Inspection Systems (MVIS) is a multidisciplinary research field that emphasizes image processing, machine vision and, pattern recognition for industrial applications. Inspection techniques are generally used in destructive and non-destructive evaluation industry. Now a day’s the current research on machine inspection gained more popularity among various researchers, because the manual assessment of the inspection may fail and turn into false assessment due to a large number of examining while inspection process.
This volume 2 covers machine learning-based approaches in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), natural language processing, medical diagnosis, etc. This edited book is designed to address various aspects of recent methodologies, concepts, and research plan out to the readers for giving more depth insights for perusing research on machine vision using machine learning-based approaches.
Table des matières
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
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
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
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
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
10.2 Experimental Materials and Methodology 196
10.2.1 Furious Si O2/Ti O2 Nanoparticle Analysis of SSBC Performance Methods 196
10.2.2 Introduction for OSELM by Use of Solar Cooker 198
10.2.3 Online Sequential Extreme Learning Machine (OSELM) Approach for Solar Cooker 199
10.2.4 OSELM Neural Network Adaptive Controller on Novel Design 199
10.2.5 Binary Search Tree Analysis of Solar Cooker 200
10.2.6 Tree Traversal of the Solar Cooker 205
10.2.7 Simulation Model of Solar Cooker Results 206
10.2.8 Program 207
10.3 Results and Discussion 210
10.4 Conclusion 212
References 214
11 Applications to Radiography and Thermography for Inspection 219
Inderjeet Singh Sandhu, Chanchal Kaushik and Mansi Chitkara
11.1 Imaging Technology and Recent Advances 220
11.2 Radiography and its Role 220
11.3 History and Discovery of X-Rays 221
11.4 Interaction of X-Rays With Matter 222
11.5 Radiographic Image Quality 222
11.6 Applications of Radiography 225
11.6.1 Computed Radiography (CR)/Digital Radiography (DR) 225
11.6.2 Fluoroscopy 227
11.6.3 DEXA 228
11.6.4 Computed Tomography 229
11.6.5 Industrial Radiography 231
11.6.6 Thermography 234
11.6.7 Veterinary Imaging 235
11.6.8 Destructive Testing 235
11.6.9 Night Vision 235
11.6.10 Conclusion 236
References 236
12 Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques 241
M. Pavithra, R. Rajmohan, T. Ananth Kumar and R. Ramya
12.1 Breast Cancer Diagnosis 242
12.2 Breast Cancer Feature Extraction 243
12.3 Machine Learning in Breast Cancer Classification 245
12.4 Image Techniques in Breast Cancer Detection 246
12.5 Dip-Based Breast Cancer Classification 248
12.6 RCNNs in Breast Cancer Prediction 255
12.7 Conclusion and Future Work 260
References 261
13 Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques 263
Vamsidhar Enireddy, Karthikeyan C., Rajesh Kumar T. and Ashok Bekkanti
13.1 Introduction 264
13.2 Related Work 265
13.2.1 Approaches in Content-Based Image Retrieval (CBIR) 265
13.2.2 Medical Image Compression 266
13.2.3 Image Retrieval for Compressed Medical Images 267
13.2.4 Feature Selection in CBIR 268
13.2.5 CBIR Using Neural Network 268
13.2.6 Classification of CBIR 269
13.3 Methodology 269
13.3.1 Huffman Coding 270
13.3.2 Haar Wavelet 271
13.3.3 Sobel Edge Detector 273
13.3.4 Gabor Filter 273
13.3.5 Proposed Hybrid CS-PSO Algorithm 276
13.4 Results and Discussion 277
13.5 Conclusion and Future Enhancement 282
13.5.1 Conclusion 282
13.5.2 Future Work 283
References 283
14 A Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay 287
Madhusudana Rao Nalluri, K. Kannan and Diptendu Sinha Roy
14.1 Introduction 288
14.2 A Brief Review of the Digital Relay Software 291
14.3 Formulating the Constrained Multi-Objective Optimization of Software Redundancy Allocation Problem (CMOO-SRAP) 293
14.3.1 Mathematical Formulation 294
14.4 The Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay 297
14.4.1 Basic Firefly Algorithm 298
14.4.2 The Modified Discrete Firefly Algorithm 299
14.4.2.1 Generating Initial Population 299
14.4.2.2 Improving Solutions 299
14.4.2.3 Illustrative Example 301
14.4.3 Similarity-Based Parent Selection (SBPS) 303
14.4.4 Solution Encoding for the CMOO-SRAP for Digital Relay Software 305
14.5 Simulation Study and Results 305
14.5.1 Simulation Environment 305
14.5.2 Simulation Parameters 306
14.5.3 Configuration of Solution Vectors for the CMOOSRAP for Digital Relay 306
14.5.4 Results and Discussion 306
14.6 Conclusion 317
References 317
Index 323
A propos de l’auteur
Muthukumaran Malarvel obtained his Ph D 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 Ph D 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.
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.
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.
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