Computer Vision Models Learning and Inference eBook Simon J D Prince PDF KSL

Computer Vision Models Learning and Inference eBook Simon J D Prince PDF OFW

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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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Computer Vision Models Learning and Inference eBook Simon J D Prince Reviews :

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

ebook,Simon J. D. Prince,Computer Vision Models, Learning, and Inference,Cambridge University Press,COMPUTERS / Computer Graphics,COMPUTERS / Computer Graphics.,COMPUTERS / Computer Vision Pattern Recognition,Computer Applications,Computer Books General,Computer Graphics,Computer Vision Pattern Recognition,Computer vision,Computer vision.,Computer/General,Computers,Computers - General Information,Computers/Software Development Engineering - Computer Graphics,Non-Fiction,Scholarly/Graduate,Textbooks (Various Levels),UNIVERSITY PRESS,United States,bisacsh,COMPUTERS / Computer Graphics,COMPUTERS / Computer Vision Pattern Recognition,Computer Graphics,Computer Vision Pattern Recognition,Computers/Software Development Engineering - Computer Graphics,Computers - General Information,bisacsh,Computers,Computer Books General,Computer vision

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Product details File Size 38611 KB Print Length 581 pages Simultaneous Device Usage Up to 4 simultaneous devices, per publisher limits Publisher Cambridge University Press; 1 edition (June 18, 2012) Publication Date June 18, 2012 Sold by  Services LLC Language English ASIN B009ZRNPEC

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