This book investigates the fundamental principles and mathematical frameworks underlying modern computer vision systems. It details the core models, learning algorithms, and inference techniques that enable machines to interpret and understand visual information, drawing upon a strong foundation in probability, statistics, and optimization.

The book is recognized as a rigorous and comprehensive treatment of the mathematical foundations of computer vision. Its depth and breadth make it a valuable resource for graduate students and researchers seeking a thorough understanding of the field's core principles. The author's expertise ensures a technically sound presentation of complex topics, positioning it as a key reference for those engaged in advanced study or development within computer vision and machine learning.