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| Autor principal: | |
|---|---|
| Format: | Recurso digital |
| Idioma: | anglès |
| Publicat: |
Zenodo
2025
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| Accés en línia: | https://doi.org/10.5281/zenodo.16720363 |
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- <h2><span>The field of computer vision has witnessed extraordinary advances over the past decade, with deep learning technologies driving a revolution in how machines interpret and understand visual data. From enabling automated medical diagnostics to powering autonomous vehicles and shaping next-generation smart devices, deep learning has transformed both the possibilities and practices in image analysis.</span></h2> <h2><span>"Deep Learning for Computer Vision: A Guide to Image Analysis" is the culmination of collaborative efforts from a team of passionate educators, researchers, and practitioners dedicated to advancing this dynamic field. With expertise spanning artificial intelligence, computer vision, neural networks, and applied data science, the contributors to this volume share a joint vision: to demystify complex concepts and empower a new generation of learners, innovators, and industry professionals.</span></h2> <h2><span>This book is designed for students, researchers, engineers, and enthusiasts who seek a clear, in-depth, and practical understanding of modern approaches to image analysis powered by deep learning. Each chapter blends foundational theory with real-world applications, reflective of our collective experiences in both academia and industry. Special emphasis is placed on hands-on learning, explainability, and ethical considerations—ensuring that readers not only build technical competency but also appreciate the broader impact and responsibility of deploying vision-based AI systems.</span></h2> <h2><span>Throughout this guide, you will find:</span></h2> <h2><span><span>·<span> </span></span></span><span>Comprehensive explanations of computer vision fundamentals, image processing, and deep learning architectures.</span></h2> <h2><span><span>·<span> </span></span></span><span>Detailed walkthroughs of classical and state-of-the-art methods including convolutional neural networks, vision transformers, and generative models.</span></h2> <h2><span><span>·<span> </span></span></span><span>Numerous case studies and practical projects drawn from medicine, robotics, remote sensing, and beyond.</span></h2> <h2><span><span>·<span> </span></span></span><span>Insights into current research frontiers, ethical dilemmas, and challenges that define the scope of contemporary computer vision.</span></h2> <h2><span>We extend our sincere gratitude to our families, institutions, colleagues, and students. Their unwavering support, critical feedback, and enthusiasm have continually inspired us to push boundaries and share knowledge.</span></h2> <h2><span>As you embark on this journey through the world of deep learning for computer vision, we hope this book will ignite curiosity, foster innovation, and serve as a reliable companion in your explorations and endeavors.</span></h2>