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 David Marshall - Cardiff School of Computer Science
From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.
by Ramakant Nevatia - Prentice-Hall
This book is about visual perception. It is based on the author's experience in teaching graduate courses in the field. It assumes no previous knowledge of the field and aims to provide a comprehensive knowledge of its methods.
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The purpose of robot vision is to enable robots to perceive the external world in order to perform a large range of tasks. This book presents a snapshot of the work in robot vision that is currently going on in different parts of the world.
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This book is a comprehensive introduction to machine vision, it will allow the reader to quickly comprehend the essentials of this topic. Emphasis is on a range of the tools and techniques for image acquisition, processing, and analysis.