{"product_id":"applied-computer-vision-through-artificial-intelligence-hardback-9781394272594","title":"Applied Computer Vision through Artificial Intelligence (Hardback) 9781394272594","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eApplied Computer Vision through Artificial Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJasminder Kaur Sandhu (Edited by), Sandhu (Author), Abhishek Kumar (Edited by), Rakesh Sahu (Edited by), Sachin Ahuja (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394272594, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 17 October 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e512 pages\u003cbr\u003e22.9 x 15.2 x 3 cm, 0.925 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eMaster 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eApplied Computer Vision through Artificial Intelligence\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eStructured 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An Overview of Medical Diagnostics through Artificial Intelligence-Powered Histopathological Imaging and Video Analysis 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAtul Rathore, Praveen Lalwani, Pooja Lalwani and Rabia Musheer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003cbr\u003e1.2 Background 11\u003cbr\u003e1.3 Preliminaries 14\u003cbr\u003e1.4 Experimental Results 24\u003cbr\u003e1.5 Conclusion 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Generative Adversarial Networks: Theory and Application in Synthesis 39\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eManoj Kumar Pandey, Priyanka Gupta, Triveni Lal Pal and Ayush Kumar Agrawal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 40\u003cbr\u003e2.2 Ideologies of GAN 45\u003cbr\u003e2.3 Architecture of GAN 47\u003cbr\u003e2.4 Applications of GAN 49\u003cbr\u003e2.5 Conclusion 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 From Pixels to Predictions: Deep Learning for Glaucoma Detection 59\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eTushar Verma, Sachin Ahuja and Jasminder Kaur Sandhu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 60\u003cbr\u003e3.2 Literature Review 67\u003cbr\u003e3.3 Problem Statement 74\u003cbr\u003e3.4 Hybrid Approach for Glaucoma Detection 75\u003cbr\u003e3.5 Result and Discussion 78\u003cbr\u003e3.6 Conclusion 84\u003cbr\u003e3.7 Future Scope 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model 89\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eN. Deepa, Padmapriya L., Priyadarshini V. and Shree Harini S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 89\u003cbr\u003e4.2 Literature Survey 90\u003cbr\u003e4.3 Proposed System 91\u003cbr\u003e4.4 Results and Discussion 93\u003cbr\u003e4.5 Conclusion 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Deep Learning-Based Detection of Cyber Extortion 99\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMohana Preya R., Ramya M. and A. Abdhur Rahman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003cbr\u003e5.2 Related Works 101\u003cbr\u003e5.3 Existing System 105\u003cbr\u003e5.4 Proposed System 106\u003cbr\u003e5.5 System Architecture 107\u003cbr\u003e5.6 Methodology 107\u003cbr\u003e5.7 Results and Discussion 112\u003cbr\u003e5.8 Conclusion 114\u003cbr\u003e5.9 Future Work 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 GANs Unleashed: From Theory to Synthetic Realities 117\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRakhi Chauhan, Priya Batta and Km Meenakshi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 117\u003cbr\u003e6.2 Related Works 122\u003cbr\u003e6.3 Limitations that are Enforced by GAN 129\u003cbr\u003e6.4 Conclusion 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 RFID and Computer Vision-Enhanced Automotive Authentication Verification System 133\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eV. Vidya Lakshmi, Sowmya M. B., Archanaa R., Shreenidhi G. and Naveena R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 134\u003cbr\u003e7.2 Literature Survey 136\u003cbr\u003e7.3 Proposed System 137\u003cbr\u003e7.4 Working 139\u003cbr\u003e7.5 Block Diagram 141\u003cbr\u003e7.6 Hardware Components 142\u003cbr\u003e7.7 Result 151\u003cbr\u003e7.8 Conclusion 153\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Synergizing Ensemble Learning Techniques for Robust Emotion Detection using EEG Signals 157\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePulkit Dwivedi, Jasminder Kaur Sandhu and Rakesh Sahu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 158\u003cbr\u003e8.2 Ensemble Learning Techniques 160\u003cbr\u003e8.3 Methodology 176\u003cbr\u003e8.4 Experimental Results 178\u003cbr\u003e8.5 Discussion 183\u003cbr\u003e8.6 Conclusion 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Understanding the Unseen: Explainability in Deep Learning for Computer Vision 187\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eApoorva Jain, Jasminder Kaur Sandhu and Pulkit Dwivedi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 188\u003cbr\u003e9.2 The Need for Interpretation in Computer Vision 190\u003cbr\u003e9.3 Understanding Interpretability in Deep Learning 192\u003cbr\u003e9.4 Visualization Techniques 195\u003cbr\u003e9.5 Maps of the Headland 200\u003cbr\u003e9.6 Model Simplification 203\u003cbr\u003e9.7 Meaning of Function 204\u003cbr\u003e9.8 Feature Importance 206\u003cbr\u003e9.9 Methods Based on Prototypes 208\u003cbr\u003e9.10 Challenges and Future Directions 208\u003cbr\u003e9.11 Conclusion 210\u003cbr\u003e9.12 Future Vision 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Prefatory Study on Landslide Susceptibility Modeling Based on Binary Random Forest Classifier 213\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eArpitha G. A. and Choodarathnakara A. L.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 214\u003cbr\u003e10.2 Materials and Methodology 215\u003cbr\u003e10.3 Result Analysis 221\u003cbr\u003e10.4 Conclusion 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Improving Digital Interactions using Augmented Reality and Computer Vision 229\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePriya Batta and Rakhi Chauhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 229\u003cbr\u003e11.2 Literature Survey 234\u003cbr\u003e11.3 Methodology 237\u003cbr\u003e11.4 Results 239\u003cbr\u003e11.5 Conclusion and Future Scope 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Evolutionary Dynamics of Machine Learning and Deep Learning Architectures in Computer Vision 243\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePalvadi Srinivas Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction to Computer Vision and Its Evolution 244\u003cbr\u003e12.2 Foundations of Machine Learning in Computer Vision 245\u003cbr\u003e12.3 Rise of Deep Learning in Computer Vision 246\u003cbr\u003e12.4 Key Architectures and Techniques in Deep Learning for Computer Vision 248\u003cbr\u003e12.5 CNN Architectures 249\u003cbr\u003e12.6 Transfer Learning and Fine-Tuning 249\u003cbr\u003e12.7 Object Detection, Image Segmentation, and Image Classification 250\u003cbr\u003e12.8 Evolution of Image Processing Models 251\u003cbr\u003e12.9 Challenges and Future Directions 256\u003cbr\u003e12.10 Applications and Impacts 261\u003cbr\u003e12.11 Conclusion 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Real-World Applications: Transforming Industries with Computer Vision 269\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSeema B. Rathod, Pallavi H. Dhole and Sivaram Ponnusamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 270\u003cbr\u003e13.2 Healthcare 273\u003cbr\u003e13.3 Manufacturing 277\u003cbr\u003e13.4 Retail 281\u003cbr\u003e13.5 Automotive 286\u003cbr\u003e13.6 Agriculture 289\u003cbr\u003e13.7 Security and Surveillance 292\u003cbr\u003e13.8 Challenges and Future Directions 295\u003cbr\u003e13.9 Future Trends 296\u003cbr\u003e13.10 Conclusion 296\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Revolutionizing Vision Perception with Multimodal Fusion Technologies 299\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePriya Batta, Rakhi Chauhan and Gagandeep Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 300\u003cbr\u003e14.2 Literature Survey 302\u003cbr\u003e14.3 Methodology 304\u003cbr\u003e14.4 Results and Discussions 306\u003cbr\u003e14.5 Conclusion and Future Scope 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Object Detection and Localization: Identifying and Pinpointing With Precision 311\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSeema B. Rathod, Pallavi H. Dhole and Sivaram Ponnusamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 312\u003cbr\u003e15.2 Background and Literature Review 315\u003cbr\u003e15.3 Methodologies and Techniques 316\u003cbr\u003e15.4 Evaluation Metrics and Benchmarks 320\u003cbr\u003e15.5 Applications and Case Studies 323\u003cbr\u003e15.6 Challenges and Future Directions 326\u003cbr\u003e15.7 Conclusion 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Uncertainty Estimation in Deep Learning Based Computer Vision 331\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePalvadi Srinivas Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 332\u003cbr\u003e16.2 Basics of Uncertainty 333\u003cbr\u003e16.3 Uncertainty Estimation Techniques 334\u003cbr\u003e16.4 Uncertainty in Object Detection 337\u003cbr\u003e16.5 Challenges and Considerations in Detecting Objects with Uncertain Predictions 338\u003cbr\u003e16.6 Case Studies and Practical Examples 338\u003cbr\u003e16.7 Uncertainty in Semantic Segmentation 339\u003cbr\u003e16.8 Pixel-Wise Uncertainty Estimation Techniques 340\u003cbr\u003e16.9 Incorporating Uncertainty Into Segmentation Models for Improved Performance 340\u003cbr\u003e16.10 Practical Implications and Case Studies 340\u003cbr\u003e16.11 Uncertainty in Image Classification 341\u003cbr\u003e16.12 Applications and Case Studies 341\u003cbr\u003e16.13 Evaluating Uncertainty Estimates 342\u003cbr\u003e16.14 Future Directions and Challenges 342\u003cbr\u003e16.15 Conclusion 346\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Overcoming Occlusions in Visual Data using Long Short-Term Memory Networks (LSTMs) 349\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSivaram Ponnusamy, K. Swaminathan, Nandha Gopal S. M., Ambika Jaiswal and Suhashini Chaurasia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 350\u003cbr\u003e17.2 Literature Survey 352\u003cbr\u003e17.3 Proposed System 353\u003cbr\u003e17.4 Results and Discussion 357\u003cbr\u003e17.5 Conclusion 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Transformative Role of Machine Learning and Deep Learning Architecture in Computer Vision 363\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNeetu Amlani, Swapnil Deshpande, Suhashini Chaurasia, Ambika Jaiswal and Sivaram Ponnusamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 364\u003cbr\u003e18.2 Literature Review 365\u003cbr\u003e18.3 Methodology 368\u003cbr\u003e18.4 Conclusion 374\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 A Comprehensive Analysis of Deep Learning and Machine Learning for Semantic Segmentation, and Object Detection in Machine and Robotic Vision 377\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePragati V. Thawani, Prafulla E. Ajmre, Suhashini Chaurasia and Sivaram Ponnusamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 378\u003cbr\u003e19.2 Machine Learning\/Deep Learning Algorithms 378\u003cbr\u003e19.3 Object Detection, Semantic Segmentation, and Human Action Recognition Methods 382\u003cbr\u003e19.4 Human and Computer Vision Systems 386\u003cbr\u003e19.5 Case Studies 388\u003cbr\u003e19.6 Challenges 389\u003cbr\u003e19.7 Conclusion 389\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 From Theoretical Foundations to Data Synthesis: Advanced Applications of Generative Adversarial Networks (GANs) 393\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePulkit Dwivedi, Jasminder Kaur Sandhu and Apoorva Jain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 393\u003cbr\u003e20.2 Theoretical Foundations of Gans 395\u003cbr\u003e20.3 Applications of GANs in Synthesis 399\u003cbr\u003e20.4 Case Studies and Practical Implementations 403\u003cbr\u003e20.5 Implementation of GANs for Synthetic Image Generation 404\u003cbr\u003e20.6 Transfer Learning in GANs 409\u003cbr\u003e20.7 Advanced Training Techniques for GANs 413\u003cbr\u003e20.8 Security Implications of GANs 418\u003cbr\u003e20.9 GANs for Sustainable AI Development 423\u003cbr\u003e20.10 Challenges and Future Directions 42720.11 Conclusion 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Optimization Techniques in Training Deep Neural Networks for Vision 433\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShantanu Bindewari, Sumit Singh Dhanda and Anand Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction to Deep Neural Networks for Vision 434\u003cbr\u003e21.2 Fundamentals of Optimization in Neural Networks 436\u003cbr\u003e21.3 Advanced Gradient-Based Optimization Techniques 438\u003cbr\u003e21.4 Regularization Techniques for Vision Models 443\u003cbr\u003e21.5 Learning Rate Schedules and Optimizers for Efficient Training 447\u003cbr\u003e21.6 Techniques for Handling Vanishing and Exploding Gradients 448\u003cbr\u003e21.7 Model Compression and Optimization for Inference 450\u003cbr\u003e21.8 Transfer Learning and Fine-Tuning Techniques 451\u003cbr\u003e21.9 Hyperparameter Tuning and Optimization Techniques 452\u003cbr\u003e21.10 Case Studies and Applications 453\u003c\/p\u003e \u003cp\u003eArchitectures 454\u003cbr\u003eReferences 455\u003cbr\u003eAbout the Editors 459\u003cbr\u003eIndex 461\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433243635992,"sku":"9781394272594","price":166.96,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394272594.jpg?v=1784852911","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/applied-computer-vision-through-artificial-intelligence-hardback-9781394272594","provider":"Freshly Printed Books","version":"1.0","type":"link"}