Computer Vision: Models, Learning, and Inference
by Simon J.D. Prince
Publisher: Cambridge University Press 2012
Number of pages: 665
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.
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by Rong-Fong Fung - InTech
This is a book about how to employ the vision theory in the market conditions for students or researchers who want to realize the technique of machine vision. The book consists of 10 chapters on different fields about vision applications.
by Asim Bhatti - InTech
The book comprehensively covers almost all aspects of stereo vision. In addition reader can find topics from defining knowledge gaps to the state of the art algorithms as well as current application trends of stereo vision.
by Kokichi Sugihara - The MIT Press
The book on computer vision which solves the problem of the interpretation of line drawings and answers many other questions regarding the errors in the placement of lines in the images. Sugihara presents a mechanism that mimics human perception.
by Jose R.A. Torreao - InTech
In this small book the authors have attempted to present a limited but relevant sample of the work being carried out in stereo vision, covering significant aspects both from the applied and from the theoretical standpoints.