Autorentext

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.

Klappentext

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.

Inhalt

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 Precisio…