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

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Computer Vision

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. â€¢ Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry â€¢ A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking â€¢ More than 70 algorithms are described in sufficient detail to implement â€¢ More than 350 full-color illustrations amplify the text â€¢ The treatment is self-contained, including all of the background mathematics â€¢ Additional resources at www.computervisionmodels.com

Country	USA
Author	Simon J. D. Prince
Binding	Kindle Edition
Edition	1
EISBN	9781139506151
Format	Kindle eBook
Label	Cambridge University Press
Manufacturer	Cambridge University Press
NumberOfPages	581
PublicationDate	2012-06-18
Publisher	Cambridge University Press
ReleaseDate	2012-11-19
Studio	Cambridge University Press

Computer Vision

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