Machine Vision Inspection Systems, Machine Learning-Based Approaches

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.

• Preface xiii1 Machine Learning-Based Virus Type Classification Using Transmission Electron Microscopy Virus Images 1Kalyan Kumar Jena, Sourav Kumar Bhoi, Soumya Ranjan Nayak and Chittaranjan Mallick1.1 Introduction 21.2 Related Works 31.3 Methodology 41.4 Results and Discussion 61.5 Conclusion 16References 162 Capsule Networks for Character Recognition in Low Resource Languages 23C. Abeysinghe, I. Perera and D.A. Meedeniya2.1 Introduction 242.2 Background Study 252.2.1 Convolutional Neural Networks 252.2.2 Related Studies on One-Shot Learning 262.2.3 Character Recognition as a One-Shot Task 262.3 System Design 282.3.1 One-Shot Learning Implementation 312.3.2 Optimization and Learning 312.3.3 Dataset 322.3.4 Training Process 322.4 Experiments and Results 332.4.1 N-Way Classification 342.4.2 Within Language Classification 372.4.3 MNIST Classification 392.4.4 Sinhala Language Classification 412.5 Discussion 412.5.1 Study Contributions 412.5.2 Challenges and Future Research Directions 422.5.3 Conclusion 43References 433 An Innovative Extended Method of Optical Pattern Recognition for Medical Images With Firm Accuracy—4f System-Based Medical Optical Pattern Recognition 47Dhivya Priya E.L., D. Jeyabharathi, K.S. Lavanya, S. Thenmozhi, R. Udaiyakumar and A. Sharmila3.1 Introduction 483.1.1 Fourier Optics 483.2 Optical Signal Processing 503.2.1 Diffraction of Light 503.2.2 Biconvex Lens 513.2.3 4f System 513.2.4 Literature Survey 523.3 Extended Medical Optical Pattern Recognition 553.3.1 Optical Fourier Transform 553.3.2 Fourier Transform Using a Lens 553.3.3 Fourier Transform in the Far Field 563.3.4 Correlator Signal Processing 563.3.5 Image Formation in 4f System 573.3.6 Extended Medical Optical Pattern Recognition 583.4 Initial 4f System 593.4.1 Extended 4f System 593.4.2 Setup of 45 Degree 593.4.3 Database Creation 593.4.4 Superimposition of Diffracted Pattern 603.4.5 Image Plane 603.5 Simulation Output 603.5.1 MATLAB 603.5.2 Sample Input Images 613.5.3 Output Simulation 613.6 Complications in Real Time Implementation 643.6.1 Database Creation 643.6.2 Accuracy 653.6.3 Optical Setup 653.7 Future Enhancements 65References 654 Brain Tumor Diagnostic System— A Deep Learning Application 69Kalaiselvi, T. and Padmapriya, S.T.4.1 Introduction 694.1.1 Intelligent Systems 694.1.2 Applied Mathematics in Machine Learning 704.1.3 Machine Learning Basics 724.1.4 Machine Learning Algorithms 734.2 Deep Learning 754.2.1 Evolution of Deep Learning 754.2.2 Deep Networks 764.2.3 Convolutional Neural Networks 774.3 Brain Tumor Diagnostic System 804.3.1 Brain Tumor 804.3.2 Methodology 804.3.3 Materials and Metrics 844.3.4 Results and Discussions 854.4 Computer-Aided Diagnostic Tool 864.5 Conclusion and Future Enhancements 87References 885 Machine Learning for Optical Character Recognition System 91Gurwinder Kaur and Tanya Garg5.1 Introduction 915.2 Character Recognition Methods 925.3 Phases of Recognition System 935.3.1 Image Acquisition 935.3.2 Defining ROI 945.3.3 Pre-Processing 945.3.4 Character Segmentation 945.3.5 Skew Detection and Correction 955.3.6 Binarization 955.3.7 Noise Removal 975.3.8 Thinning 975.3.9 Representation 975.3.10 Feature Extraction 985.3.11 Training and Recognition 985.4 Post-Processing 1015.5 Performance Evaluation 1035.5.1 Recognition Rate 1035.5.2 Rejection Rate 1035.5.3 Error Rate 1035.6 Applications of OCR Systems 1045.7 Conclusion and Future Scope 105References 1056 Surface Defect Detection Using SVM-Based Machine Vision System with Optimized Feature 109Ashok Kumar Patel, Venkata Naresh Mandhala, Dinesh Kumar Anguraj and Soumya Ranjan Nayak6.1 Introduction 1106.2 Methodology 1136.2.1 Data Collection 1136.2.2 Data Pre-Processing 1136.2.3 Feature Extraction 1156.2.4 Feature Optimization 1166.2.5 Model Development 1196.2.6 Performance Evaluation 1206.3 Conclusion 123References 1247 Computational Linguistics-Based Tamil Character Recognition System for Text to Speech Conversion 129Suriya, S., Balaji, M., Gowtham, T.M. and Rahul, Kumar S.7.1 Introduction 1307.2 Literature Survey 1307.3 Proposed Approach 1347.4 Design and Analysis 1347.5 Experimental Setup and Implementation 1367.6 Conclusion 151References 1518 A Comparative Study of Different Classifiers to Propose a GONN for Breast Cancer Detection 155Ankita Tiwari, Bhawana Sahu, Jagalingam Pushaparaj and Muthukumaran Malarvel8.1 Introduction 1568.2 Methodology 1578.2.1 Dataset 1578.2.2 Linear Regression 1598.2.2.1 Correlation 1608.2.2.2 Covariance 1608.2.3 Classification Algorithm 1618.2.3.1 Support Vector Machine 1618.2.3.2 Random Forest Classifier 1628.2.3.3 K-Nearest Neighbor Classifier 1638.2.3.4 Decision Tree Classifier 1638.2.3.5 Multi-Layered Perceptron 1648.3 Results and Discussion 1658.4 Conclusion 169References 1699 Mexican Sign-Language Static-Alphabet Recognition Using 3D Affine Invariants 171Guadalupe Carmona-Arroyo, Homero V. Rios-Figueroa and Martha Lorena Avendaño-Garrido9.1 Introduction 1719.2 Pattern Recognition 1759.2.1 3D Affine Invariants 1759.3 Experiments 1779.3.1 Participants 1799.3.2 Data Acquisition 1799.3.3 Data Augmentation 1799.3.4 Feature Extraction 1819.3.5 Classification 1819.4 Results 1829.4.1 Experiment 1 1829.4.2 Experiment 2 1849.4.3 Experiment 3 1849.5 Discussion 1889.6 Conclusion 189Acknowledgments 190References 19010 Performance of Stepped Bar Plate-Coated Nanolayer of a Box Solar Cooker Control Based on Adaptive Tree Traversal Energy and OSELM 193S. Shanmugan, F.A. Essa, J. Nagaraj and Shilpa Itnal10.1 Introduction 19410.2 Experimental Materials and Methodology 19610.2.1 Furious SiO2/TiO2 Nanoparticle Analysis of SSBC Performance Methods 19610.2.2 Introduction for OSELM by Use of Solar Cooker 19810.2.3 Online Sequential Extreme Learning Machine (OSELM) Approach for Solar Cooker 19910.2.4 OSELM Neural Network Adaptive Controller on Novel Design 19910.2.5 Binary Search Tree Analysis of Solar Cooker 20010.2.6 Tree Traversal of the Solar Cooker 20510.2.7 Simulation Model of Solar Cooker Results 20610.2.8 Program 20710.3 Results and Discussion 21010.4 Conclusion 212References 21411 Applications to Radiography and Thermography for Inspection 219Inderjeet Singh Sandhu, Chanchal Kaushik and Mansi Chitkara11.1 Imaging Technology and Recent Advances 22011.2 Radiography and its Role 22011.3 History and Discovery of X-Rays 22111.4 Interaction of X-Rays With Matter 22211.5 Radiographic Image Quality 22211.6 Applications of Radiography 22511.6.1 Computed Radiography (CR)/Digital Radiography (DR) 22511.6.2 Fluoroscopy 22711.6.3 DEXA 22811.6.4 Computed Tomography 22911.6.5 Industrial Radiography 23111.6.6 Thermography 23411.6.7 Veterinary Imaging 23511.6.8 Destructive Testing 23511.6.9 Night Vision 23511.6.10 Conclusion 236References 23612 Prediction and Classification of Breast Cancer Using Discriminative Learning Models and Techniques 241M. Pavithra, R. Rajmohan, T. Ananth Kumar and R. Ramya12.1 Breast Cancer Diagnosis 24212.2 Breast Cancer Feature Extraction 24312.3 Machine Learning in Breast Cancer Classification 24512.4 Image Techniques in Breast Cancer Detection 24612.5 Dip-Based Breast Cancer Classification 24812.6 RCNNs in Breast Cancer Prediction 25512.7 Conclusion and Future Work 260References 26113 Compressed Medical Image Retrieval Using Data Mining and Optimized Recurrent Neural Network Techniques 263Vamsidhar Enireddy, Karthikeyan C., Rajesh Kumar T. and Ashok Bekkanti13.1 Introduction 26413.2 Related Work 26513.2.1 Approaches in Content-Based Image Retrieval (CBIR) 26513.2.2 Medical Image Compression 26613.2.3 Image Retrieval for Compressed Medical Images 26713.2.4 Feature Selection in CBIR 26813.2.5 CBIR Using Neural Network 26813.2.6 Classification of CBIR 26913.3 Methodology 26913.3.1 Huffman Coding 27013.3.2 Haar Wavelet 27113.3.3 Sobel Edge Detector 27313.3.4 Gabor Filter 27313.3.5 Proposed Hybrid CS-PSO Algorithm 27613.4 Results and Discussion 27713.5 Conclusion and Future Enhancement 28213.5.1 Conclusion 28213.5.2 Future Work 283References 28314 A Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay 287Madhusudana Rao Nalluri, K. Kannan and Diptendu Sinha Roy14.1 Introduction 28814.2 A Brief Review of the Digital Relay Software 29114.3 Formulating the Constrained Multi-Objective Optimization of Software Redundancy Allocation Problem (CMOO-SRAP) 29314.3.1 Mathematical Formulation 29414.4 The Novel Discrete Firefly Algorithm for Constrained Multi-Objective Software Reliability Assessment of Digital Relay 29714.4.1 Basic Firefly Algorithm 29814.4.2 The Modified Discrete Firefly Algorithm 29914.4.2.1 Generating Initial Population 29914.4.2.2 Improving Solutions 29914.4.2.3 Illustrative Example 30114.4.3 Similarity-Based Parent Selection (SBPS) 30314.4.4 Solution Encoding for the CMOO-SRAP for Digital Relay Software 30514.5 Simulation Study and Results 30514.5.1 Simulation Environment 30514.5.2 Simulation Parameters 30614.5.3 Configuration of Solution Vectors for the CMOOSRAP for Digital Relay 30614.5.4 Results and Discussion 30614.6 Conclusion 317References 317Index 323