Applied Computer Vision through Artificial Intelligence

Master the cutting-edge field of computer vision and artificial intelligence with this accessible guide to the applications of machine learning and deep learning for real-world solutions in robotics, healthcare, and autonomous systems. Applied Computer Vision through Artificial Intelligence provides a thorough and accessible exploration of how machine learning and deep learning are driving breakthroughs in computer vision. This book brings together contributions from leading experts to present state-of-the-art techniques, tools, and frameworks, while demonstrating this technology’s applications in healthcare, autonomous systems, surveillance, robotics, and other real-world domains. By blending theory with hands-on insights, this volume equips readers with the knowledge needed to understand, design, and implement AI-powered vision solutions. Structured to serve both academic and professional audiences, the book not only covers cutting-edge algorithms and methodologies but also addresses pressing challenges, ethical considerations, and future research directions. It serves as a comprehensive reference for researchers, engineers, practitioners, and graduate students, making it an indispensable resource for anyone looking to apply artificial intelligence to solve complex computer vision problems in today’s data-driven world.

Table of Contents:
Preface xxi 1 An Overview of Medical Diagnostics through Artificial Intelligence-Powered Histopathological Imaging and Video Analysis 1 Atul Rathore, Praveen Lalwani, Pooja Lalwani and Rabia Musheer 1.1 Introduction 2 1.2 Background 11 1.3 Preliminaries 14 1.4 Experimental Results 24 1.5 Conclusion 30 2 Generative Adversarial Networks: Theory and Application in Synthesis 39 Manoj Kumar Pandey, Priyanka Gupta, Triveni Lal Pal and Ayush Kumar Agrawal 2.1 Introduction 40 2.2 Ideologies of GAN 45 2.3 Architecture of GAN 47 2.4 Applications of GAN 49 2.5 Conclusion 55 3 From Pixels to Predictions: Deep Learning for Glaucoma Detection 59 Tushar Verma, Sachin Ahuja and Jasminder Kaur Sandhu 3.1 Introduction 60 3.2 Literature Review 67 3.3 Problem Statement 74 3.4 Hybrid Approach for Glaucoma Detection 75 3.5 Result and Discussion 78 3.6 Conclusion 84 3.7 Future Scope 84 4 Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model 89 N. Deepa, Padmapriya L., Priyadarshini V. and Shree Harini S. 4.1 Introduction 89 4.2 Literature Survey 90 4.3 Proposed System 91 4.4 Results and Discussion 93 4.5 Conclusion 96 5 Deep Learning-Based Detection of Cyber Extortion 99 Mohana Preya R., Ramya M. and A. Abdhur Rahman 5.1 Introduction 100 5.2 Related Works 101 5.3 Existing System 105 5.4 Proposed System 106 5.5 System Architecture 107 5.6 Methodology 107 5.7 Results and Discussion 112 5.8 Conclusion 114 5.9 Future Work 114 6 GANs Unleashed: From Theory to Synthetic Realities 117 Rakhi Chauhan, Priya Batta and Km Meenakshi 6.1 Introduction 117 6.2 Related Works 122 6.3 Limitations that are Enforced by GAN 129 6.4 Conclusion 130 7 RFID and Computer Vision-Enhanced Automotive Authentication Verification System 133 V. Vidya Lakshmi, Sowmya M. B., Archanaa R., Shreenidhi G. and Naveena R. 7.1 Introduction 134 7.2 Literature Survey 136 7.3 Proposed System 137 7.4 Working 139 7.5 Block Diagram 141 7.6 Hardware Components 142 7.7 Result 151 7.8 Conclusion 153 8 Synergizing Ensemble Learning Techniques for Robust Emotion Detection using EEG Signals 157 Pulkit Dwivedi, Jasminder Kaur Sandhu and Rakesh Sahu 8.1 Introduction 158 8.2 Ensemble Learning Techniques 160 8.3 Methodology 176 8.4 Experimental Results 178 8.5 Discussion 183 8.6 Conclusion 185 9 Understanding the Unseen: Explainability in Deep Learning for Computer Vision 187 Apoorva Jain, Jasminder Kaur Sandhu and Pulkit Dwivedi 9.1 Introduction 188 9.2 The Need for Interpretation in Computer Vision 190 9.3 Understanding Interpretability in Deep Learning 192 9.4 Visualization Techniques 195 9.5 Maps of the Headland 200 9.6 Model Simplification 203 9.7 Meaning of Function 204 9.8 Feature Importance 206 9.9 Methods Based on Prototypes 208 9.10 Challenges and Future Directions 208 9.11 Conclusion 210 9.12 Future Vision 211 10 Prefatory Study on Landslide Susceptibility Modeling Based on Binary Random Forest Classifier 213 Arpitha G. A. and Choodarathnakara A. L. 10.1 Introduction 214 10.2 Materials and Methodology 215 10.3 Result Analysis 221 10.4 Conclusion 224 11 Improving Digital Interactions using Augmented Reality and Computer Vision 229 Priya Batta and Rakhi Chauhan 11.1 Introduction 229 11.2 Literature Survey 234 11.3 Methodology 237 11.4 Results 239 11.5 Conclusion and Future Scope 240 12 The Evolutionary Dynamics of Machine Learning and Deep Learning Architectures in Computer Vision 243 Palvadi Srinivas Kumar 12.1 Introduction to Computer Vision and Its Evolution 244 12.2 Foundations of Machine Learning in Computer Vision 245 12.3 Rise of Deep Learning in Computer Vision 246 12.4 Key Architectures and Techniques in Deep Learning for Computer Vision 248 12.5 CNN Architectures 249 12.6 Transfer Learning and Fine-Tuning 249 12.7 Object Detection, Image Segmentation, and Image Classification 250 12.8 Evolution of Image Processing Models 251 12.9 Challenges and Future Directions 256 12.10 Applications and Impacts 261 12.11 Conclusion 265 13 Real-World Applications: Transforming Industries with Computer Vision 269 Seema B. Rathod, Pallavi H. Dhole and Sivaram Ponnusamy 13.1 Introduction 270 13.2 Healthcare 273 13.3 Manufacturing 277 13.4 Retail 281 13.5 Automotive 286 13.6 Agriculture 289 13.7 Security and Surveillance 292 13.8 Challenges and Future Directions 295 13.9 Future Trends 296 13.10 Conclusion 296 14 Revolutionizing Vision Perception with Multimodal Fusion Technologies 299 Priya Batta, Rakhi Chauhan and Gagandeep Kaur 14.1 Introduction 300 14.2 Literature Survey 302 14.3 Methodology 304 14.4 Results and Discussions 306 14.5 Conclusion and Future Scope 308 15 Object Detection and Localization: Identifying and Pinpointing With Precision 311 Seema B. Rathod, Pallavi H. Dhole and Sivaram Ponnusamy 15.1 Introduction 312 15.2 Background and Literature Review 315 15.3 Methodologies and Techniques 316 15.4 Evaluation Metrics and Benchmarks 320 15.5 Applications and Case Studies 323 15.6 Challenges and Future Directions 326 15.7 Conclusion 328 16 Uncertainty Estimation in Deep Learning Based Computer Vision 331 Palvadi Srinivas Kumar 16.1 Introduction 332 16.2 Basics of Uncertainty 333 16.3 Uncertainty Estimation Techniques 334 16.4 Uncertainty in Object Detection 337 16.5 Challenges and Considerations in Detecting Objects with Uncertain Predictions 338 16.6 Case Studies and Practical Examples 338 16.7 Uncertainty in Semantic Segmentation 339 16.8 Pixel-Wise Uncertainty Estimation Techniques 340 16.9 Incorporating Uncertainty Into Segmentation Models for Improved Performance 340 16.10 Practical Implications and Case Studies 340 16.11 Uncertainty in Image Classification 341 16.12 Applications and Case Studies 341 16.13 Evaluating Uncertainty Estimates 342 16.14 Future Directions and Challenges 342 16.15 Conclusion 346 17 Overcoming Occlusions in Visual Data using Long Short-Term Memory Networks (LSTMs) 349 Sivaram Ponnusamy, K. Swaminathan, Nandha Gopal S. M., Ambika Jaiswal and Suhashini Chaurasia 17.1 Introduction 350 17.2 Literature Survey 352 17.3 Proposed System 353 17.4 Results and Discussion 357 17.5 Conclusion 360 18 Transformative Role of Machine Learning and Deep Learning Architecture in Computer Vision 363 Neetu Amlani, Swapnil Deshpande, Suhashini Chaurasia, Ambika Jaiswal and Sivaram Ponnusamy 18.1 Introduction 364 18.2 Literature Review 365 18.3 Methodology 368 18.4 Conclusion 374 19 A Comprehensive Analysis of Deep Learning and Machine Learning for Semantic Segmentation, and Object Detection in Machine and Robotic Vision 377 Pragati V. Thawani, Prafulla E. Ajmre, Suhashini Chaurasia and Sivaram Ponnusamy 19.1 Introduction 378 19.2 Machine Learning/Deep Learning Algorithms 378 19.3 Object Detection, Semantic Segmentation, and Human Action Recognition Methods 382 19.4 Human and Computer Vision Systems 386 19.5 Case Studies 388 19.6 Challenges 389 19.7 Conclusion 389 20 From Theoretical Foundations to Data Synthesis: Advanced Applications of Generative Adversarial Networks (GANs) 393 Pulkit Dwivedi, Jasminder Kaur Sandhu and Apoorva Jain 20.1 Introduction 393 20.2 Theoretical Foundations of Gans 395 20.3 Applications of GANs in Synthesis 399 20.4 Case Studies and Practical Implementations 403 20.5 Implementation of GANs for Synthetic Image Generation 404 20.6 Transfer Learning in GANs 409 20.7 Advanced Training Techniques for GANs 413 20.8 Security Implications of GANs 418 20.9 GANs for Sustainable AI Development 423 20.10 Challenges and Future Directions 42720.11 Conclusion 430 21 Optimization Techniques in Training Deep Neural Networks for Vision 433 Shantanu Bindewari, Sumit Singh Dhanda and Anand Singh 21.1 Introduction to Deep Neural Networks for Vision 434 21.2 Fundamentals of Optimization in Neural Networks 436 21.3 Advanced Gradient-Based Optimization Techniques 438 21.4 Regularization Techniques for Vision Models 443 21.5 Learning Rate Schedules and Optimizers for Efficient Training 447 21.6 Techniques for Handling Vanishing and Exploding Gradients 448 21.7 Model Compression and Optimization for Inference 450 21.8 Transfer Learning and Fine-Tuning Techniques 451 21.9 Hyperparameter Tuning and Optimization Techniques 452 21.10 Case Studies and Applications 453 Architectures 454 References 455 About the Editors 459 Index 461

About the Author :
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.