Description
Computer Vision: Models, Learning, and Inference presents a modern approach to computer vision by integrating probabilistic modeling, machine learning, and statistical inference into a unified framework. Rather than focusing solely on vision algorithms, the book explains how visual information can be represented, interpreted, and learned from data to solve tasks such as image classification, object recognition, segmentation, tracking, three-dimensional reconstruction, and scene understanding. Combining rigorous mathematical foundations with practical examples, it emphasizes the role of learning-based methods in contemporary computer vision and provides readers with a solid understanding of both the theory and practice of intelligent visual systems. It is an essential resource for students, researchers, and professionals in computer vision, artificial intelligence, and machine learning.
