Applied Computer Vision through Artificial Intelligence

Jasminder Kaur Sandhu, PhD is a professor and the Head of the Department of Machine Learning and Data Science at IILM University. With over 13 years of academic and research experience, she has published more than 70 research papers in reputed international journals. Her research interests include machine learning, ensemble modelling, artificial intelligence, wireless sensor networks, and soft computing. Abhishek Kumar, PhD is a professor and the Assistant Director of the Computer Science and Engineering Department at Chandigarh University, Punjab with over 13 years of teaching experience. He is an award-winning researcher that has published more than 170 peer-reviewed papers in international journals of repute. His research interests span artificial intelligence, renewable energy systems, image processing, and data mining. Rakesh Sahu, PhD is a dedicated academician and researcher with over a decade of experience. He has made significant contributions as a post-doctoral scholar at IIT Bombay and as a faculty member at esteemed institutions, where his work focuses on Himalayan glacier dynamics. His research interests include glacier mapping, modelling, and climate change. Sachin Ahuja, PhD has an illustrious academic and research career, marked by numerous impactful contributions. An accomplished editor, he has contributed to numerous books and served as a guest editor for special issues in reputed international journals. His research focuses on artificial intelligence, machine learning, and data mining.

• Preface xxi1 An Overview of Medical Diagnostics through Artificial Intelligence-Powered Histopathological Imaging and Video Analysis 1Atul Rathore, Praveen Lalwani, Pooja Lalwani and Rabia Musheer1.1 Introduction 21.2 Background 111.3 Preliminaries 141.4 Experimental Results 241.5 Conclusion 302 Generative Adversarial Networks: Theory and Application in Synthesis 39Manoj Kumar Pandey, Priyanka Gupta, Triveni Lal Pal and Ayush Kumar Agrawal2.1 Introduction 402.2 Ideologies of GAN 452.3 Architecture of GAN 472.4 Applications of GAN 492.5 Conclusion 553 From Pixels to Predictions: Deep Learning for Glaucoma Detection 59Tushar Verma, Sachin Ahuja and Jasminder Kaur Sandhu3.1 Introduction 603.2 Literature Review 673.3 Problem Statement 743.4 Hybrid Approach for Glaucoma Detection 753.5 Result and Discussion 783.6 Conclusion 843.7 Future Scope 844 Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model 89N. Deepa, Padmapriya L., Priyadarshini V. and Shree Harini S.4.1 Introduction 894.2 Literature Survey 904.3 Proposed System 914.4 Results and Discussion 934.5 Conclusion 965 Deep Learning-Based Detection of Cyber Extortion 99Mohana Preya R., Ramya M. and A. Abdhur Rahman5.1 Introduction 1005.2 Related Works 1015.3 Existing System 1055.4 Proposed System 1065.5 System Architecture 1075.6 Methodology 1075.7 Results and Discussion 1125.8 Conclusion 1145.9 Future Work 1146 GANs Unleashed: From Theory to Synthetic Realities 117Rakhi Chauhan, Priya Batta and Km Meenakshi6.1 Introduction 1176.2 Related Works 1226.3 Limitations that are Enforced by GAN 1296.4 Conclusion 1307 RFID and Computer Vision-Enhanced Automotive Authentication Verification System 133V. Vidya Lakshmi, Sowmya M. B., Archanaa R., Shreenidhi G. and Naveena R.7.1 Introduction 1347.2 Literature Survey 1367.3 Proposed System 1377.4 Working 1397.5 Block Diagram 1417.6 Hardware Components 1427.7 Result 1517.8 Conclusion 1538 Synergizing Ensemble Learning Techniques for Robust Emotion Detection using EEG Signals 157Pulkit Dwivedi, Jasminder Kaur Sandhu and Rakesh Sahu8.1 Introduction 1588.2 Ensemble Learning Techniques 1608.3 Methodology 1768.4 Experimental Results 1788.5 Discussion 1838.6 Conclusion 1859 Understanding the Unseen: Explainability in Deep Learning for Computer Vision 187Apoorva Jain, Jasminder Kaur Sandhu and Pulkit Dwivedi9.1 Introduction 1889.2 The Need for Interpretation in Computer Vision 1909.3 Understanding Interpretability in Deep Learning 1929.4 Visualization Techniques 1959.5 Maps of the Headland 2009.6 Model Simplification 2039.7 Meaning of Function 2049.8 Feature Importance 2069.9 Methods Based on Prototypes 2089.10 Challenges and Future Directions 2089.11 Conclusion 2109.12 Future Vision 21110 Prefatory Study on Landslide Susceptibility Modeling Based on Binary Random Forest Classifier 213Arpitha G. A. and Choodarathnakara A. L.10.1 Introduction 21410.2 Materials and Methodology 21510.3 Result Analysis 22110.4 Conclusion 22411 Improving Digital Interactions using Augmented Reality and Computer Vision 229Priya Batta and Rakhi Chauhan11.1 Introduction 22911.2 Literature Survey 23411.3 Methodology 23711.4 Results 23911.5 Conclusion and Future Scope 24012 The Evolutionary Dynamics of Machine Learning and Deep Learning Architectures in Computer Vision 243Palvadi Srinivas Kumar12.1 Introduction to Computer Vision and Its Evolution 24412.2 Foundations of Machine Learning in Computer Vision 24512.3 Rise of Deep Learning in Computer Vision 24612.4 Key Architectures and Techniques in Deep Learning for Computer Vision 24812.5 CNN Architectures 24912.6 Transfer Learning and Fine-Tuning 24912.7 Object Detection, Image Segmentation, and Image Classification 25012.8 Evolution of Image Processing Models 25112.9 Challenges and Future Directions 25612.10 Applications and Impacts 26112.11 Conclusion 26513 Real-World Applications: Transforming Industries with Computer Vision 269Seema B. Rathod, Pallavi H. Dhole and Sivaram Ponnusamy13.1 Introduction 27013.2 Healthcare 27313.3 Manufacturing 27713.4 Retail 28113.5 Automotive 28613.6 Agriculture 28913.7 Security and Surveillance 29213.8 Challenges and Future Directions 29513.9 Future Trends 29613.10 Conclusion 29614 Revolutionizing Vision Perception with Multimodal Fusion Technologies 299Priya Batta, Rakhi Chauhan and Gagandeep Kaur14.1 Introduction 30014.2 Literature Survey 30214.3 Methodology 30414.4 Results and Discussions 30614.5 Conclusion and Future Scope 30815 Object Detection and Localization: Identifying and Pinpointing With Precision 311Seema B. Rathod, Pallavi H. Dhole and Sivaram Ponnusamy15.1 Introduction 31215.2 Background and Literature Review 31515.3 Methodologies and Techniques 31615.4 Evaluation Metrics and Benchmarks 32015.5 Applications and Case Studies 32315.6 Challenges and Future Directions 32615.7 Conclusion 32816 Uncertainty Estimation in Deep Learning Based Computer Vision 331Palvadi Srinivas Kumar16.1 Introduction 33216.2 Basics of Uncertainty 33316.3 Uncertainty Estimation Techniques 33416.4 Uncertainty in Object Detection 33716.5 Challenges and Considerations in Detecting Objects with Uncertain Predictions 33816.6 Case Studies and Practical Examples 33816.7 Uncertainty in Semantic Segmentation 33916.8 Pixel-Wise Uncertainty Estimation Techniques 34016.9 Incorporating Uncertainty Into Segmentation Models for Improved Performance 34016.10 Practical Implications and Case Studies 34016.11 Uncertainty in Image Classification 34116.12 Applications and Case Studies 34116.13 Evaluating Uncertainty Estimates 34216.14 Future Directions and Challenges 34216.15 Conclusion 34617 Overcoming Occlusions in Visual Data using Long Short-Term Memory Networks (LSTMs) 349Sivaram Ponnusamy, K. Swaminathan, Nandha Gopal S. M., Ambika Jaiswal and Suhashini Chaurasia17.1 Introduction 35017.2 Literature Survey 35217.3 Proposed System 35317.4 Results and Discussion 35717.5 Conclusion 36018 Transformative Role of Machine Learning and Deep Learning Architecture in Computer Vision 363Neetu Amlani, Swapnil Deshpande, Suhashini Chaurasia, Ambika Jaiswal and Sivaram Ponnusamy18.1 Introduction 36418.2 Literature Review 36518.3 Methodology 36818.4 Conclusion 37419 A Comprehensive Analysis of Deep Learning and Machine Learning for Semantic Segmentation, and Object Detection in Machine and Robotic Vision 377Pragati V. Thawani, Prafulla E. Ajmre, Suhashini Chaurasia and Sivaram Ponnusamy19.1 Introduction 37819.2 Machine Learning/Deep Learning Algorithms 37819.3 Object Detection, Semantic Segmentation, and Human Action Recognition Methods 38219.4 Human and Computer Vision Systems 38619.5 Case Studies 38819.6 Challenges 38919.7 Conclusion 38920 From Theoretical Foundations to Data Synthesis: Advanced Applications of Generative Adversarial Networks (GANs) 393Pulkit Dwivedi, Jasminder Kaur Sandhu and Apoorva Jain20.1 Introduction 39320.2 Theoretical Foundations of Gans 39520.3 Applications of GANs in Synthesis 39920.4 Case Studies and Practical Implementations 40320.5 Implementation of GANs for Synthetic Image Generation 40420.6 Transfer Learning in GANs 40920.7 Advanced Training Techniques for GANs 41320.8 Security Implications of GANs 41820.9 GANs for Sustainable AI Development 42320.10 Challenges and Future Directions 42720.11 Conclusion 43021 Optimization Techniques in Training Deep Neural Networks for Vision 433Shantanu Bindewari, Sumit Singh Dhanda and Anand Singh21.1 Introduction to Deep Neural Networks for Vision 43421.2 Fundamentals of Optimization in Neural Networks 43621.3 Advanced Gradient-Based Optimization Techniques 43821.4 Regularization Techniques for Vision Models 44321.5 Learning Rate Schedules and Optimizers for Efficient Training 44721.6 Techniques for Handling Vanishing and Exploding Gradients 44821.7 Model Compression and Optimization for Inference 45021.8 Transfer Learning and Fine-Tuning Techniques 45121.9 Hyperparameter Tuning and Optimization Techniques 45221.10 Case Studies and Applications 453Architectures 454References 455About the Editors 459Index 461