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Computer Vision : Models, Learning, and Inference

Computer Vision : Models, Learning, and Inference

Computer Vision : Models, Learning, and Inference Hardback - 2012

by Prince, Simon J. D

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Description

ISBN: 9781107011793

Cambridge University Press, 22 March 2019

Hardback, 598 pages

Description

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

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Details

  • Title Computer Vision : Models, Learning, and Inference
  • Author Prince, Simon J. D
  • Binding Hardback
  • Edition Hardback
  • Pages 598
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2012-06-18
  • Features Bibliography, Index
  • Bookseller's Inventory # 9781107011793
  • ISBN 9781107011793 / 1107011795
  • Weight 3.1 lbs (1.41 kg)
  • Dimensions 10.1 x 7 x 1.3 in (25.65 x 17.78 x 3.30 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - General Information
  • Library of Congress subjects Computer vision, COMPUTERS / Computer Graphics
  • Library of Congress Catalogue Number 2012008187
  • Dewey Decimal Code 006.37
  • Quantity available 3

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From the publisher

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

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