Computer Vision: Models, Learning, and Inference

Simon J. D. Prince

ISBN 10: 1107011795 / ISBN 13: 9781107011793

Published by Cambridge University Press, 2012

About this Item

2012. 1st Edition. Hardcover. A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms. Num Pages: 598 pages, 357 colour illus. 5 tables 201 exercises. BIC Classification: UYQV. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 255 x 187 x 32. Weight in Grams: 1428. Models, Learning, and Inference. 598 pages, 357 colour illus. 5 tables 201 exercises. A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: UYQV. Dimension: 255 x 187 x 32. Weight: 1422. . . . . . Books ship from the US and Ireland.

Seller Inventory # V9781107011793

Bibliographic Details

• Title: Computer Vision: Models, Learning, and Inference
• Publisher: Cambridge University Press
• Publication Date: 2012
• Language: English
• ISBN 10: 1107011795
• ISBN 13: 9781107011793
• Binding: Hardcover
• Condition: New

About this title

Synopsis

"This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. [bullet] Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry [bullet] A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking [bullet] More than 70 algorithms are described in sufficient detail to implement [bullet] More than 350 full-color illustrations amplify the text [bullet] The treatment is self-contained, including all of the background mathematics [bullet] Additional resources at www.computervisionmodels.com"--

About the Author

Dr Simon J. D. Prince is a faculty member in the Department of Computer Science at University College London. He has taught courses on machine vision, image processing and advanced mathematical methods. He has a diverse background in biological and computing sciences and has published papers across the fields of computer vision, biometrics, psychology, physiology, medical imaging, computer graphics and HCI.