Computer Vision: A Modern Approach
Computer Vision: A Modern Approach explores how machines can interpret and understand visual information from the world, replicating the complex processes of human vision through computational methods. This interdisciplinary field draws from mathematics, artificial intelligence, physics, and neuroscience to create systems that can analyze images and video, detect patterns, recognize objects, and make decisions based on visual data. The book emphasizes both theoretical foundations and practical algorithms, offering readers a comprehensive introduction to the science and engineering of computer vision. Core topics include image formation, feature detection, segmentation, motion analysis, and 3D reconstruction. Techniques such as edge detection, optical flow, stereo matching, and object recognition are discussed in detail, with mathematical rigor and application-oriented insights. The book also explores learning-based approaches, including the integration of deep learning models that have transformed modern computer vision applications. Designed for students, researchers, and engineers, the text balances theoretical depth with real-world relevance. From autonomous vehicles and medical imaging to surveillance systems and augmented reality, the concepts taught in this book are critical to developing intelligent systems capable of visual perception. Computer Vision: A Modern Approach remains a leading resource for anyone aiming to understand or innovate in this dynamic and rapidly evolving field. "Computer Vision: A Modern Approach" is a comprehensive textbook that blends foundational theory with practical techniques for teaching machines to interpret visual data. Contents: 1. Computer Vision, 2. Computer Graphics Design and Motion, 3. Digital Image, 4. Concept of Computer Images, 5. Artificial Intelligence Systems in Computer Projects, 6. Computer Image Scanner, 7. 3D Transformation, 8. Digital Camera, 9. Computer Scanography, 10. Web Multimedia, 11. Graph Colouring.