Read Computer Vision: Models, Learning, and Inference

Computer Vision: Models, Learning, and Inference

Simon J.D. Prince

4.9/5 (19311 ratings)

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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Computer Vision: Models, Learning, and Inference. To get started finding Computer Vision: Models, Learning, and Inference, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

Pages: 600

Format: PDF, EPUB & Kindle Edition

Publisher: Cambridge University Press

Release: 2012

ISBN: 1107011795

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.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Computer Vision: Models, Learning, and Inference. To get started finding Computer Vision: Models, Learning, and Inference, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Computer Vision: Models, Learning, and Inference

Computer Vision: Models, Learning, and Inference

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