Computer Vision : Models, Learning, and Inference (Hardcover)

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Specifications

Publisher Cambridge Univ Pr
Mfg Part# 9781107011793
SKU 229371914
Format Hardcover
ISBN10 1107011795
Release Date 6/18/2012
Physical
Dimensions (in Inches) 10.5H x 7.5L x 1.25T
From the Publisher
Editors Note "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"--
Product Attributes
eBooks Kobo
Book Format Hardcover
Number of Pages 0598
Publisher Cambridge University Press

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