Computer Vision: Models, Learning, and Inference (Hardback)Simon J. D. Prince (author)
Publisher: Cambridge University Press
Number of pages: 598
Weight: 1400 g
Dimensions: 260 x 184 x 28 mm
'With clarity and depth, this book introduces the mathematical foundations of probabilistic models for computer vision, all with well-motivated, concrete examples and applications. Most modern computer vision texts focus on visual tasks; Prince's beautiful new book is natural complement, focusing squarely on fundamental techniques, emphasizing models and associated methods for learning and inference. I think every serious student and researcher will find this book valuable. I've been using draft chapters of this remarkable book in my vision and learning courses for more than two years. It will remain a staple of mine for years to come.' David J. Fleet, University of Toronto
'This book addresses the fundamentals of how we make progress in this challenging and exciting field. I look forward to many decades with [this book] on my shelf, or indeed, I suspect, open on my desktop.' Andrew Fitzgibbon, from the Foreword
'Prince's magnum opus provides a fully probabilistic framework for understanding modern computer vision. With straightforward descriptions, insightful figures, example applications, exercises, background mathematics, and pseudocode, this book is self-contained and has all that is needed to explore this fascinating discipline.' Roberto Cipolla, University of Cambridge
'The author's goal, as stated in the preface, is to provide a book that focuses on the models involved, and I think the book has succeeded in doing that. I learned quite a bit and would recommend this text highly to the motivated, mathematically mature reader.' Jeffrey Putnam, Computing Reviews
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