Machine learning for computer vision
Author(s)
Bibliographic Information
Machine learning for computer vision
（Studies in computational intelligence, 411）
Springer, c2013
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Note
Based on talks and tutorials from recent International Computer Vision Summer Schools
Includes bibliographical references
Description and Table of Contents
Description
Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.
Table of Contents
Throwing Down the Visual Intelligence Gauntlet.- Actionable Information in Vision.- Learning Binary Hash Codes for Large-Scale Image Search.- Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant
Object Recognition.- Real-Time Human Pose Recognition in Parts from Single Depth Images.- Scale-Invariant Vote-based 3D Recognition and Registration from Point Clouds.- Multiple Classifier Boosting and Tree-Structured Classifiers.- Simultaneous detection and tracking with multiple cameras.- Applications of Computer Vision to Vehicles: an extreme test.
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