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…mehr
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Muthukumaran Malarvel obtained his PhD 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 PhD 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 PhD (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.
Inhaltsangabe
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
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
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