Machine Learning for Computer Vision

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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.

Author(s): Cheston Tan, Joel Z. Leibo, Tomaso Poggio (auth.), Roberto Cipolla, Sebastiano Battiato, Giovanni Maria Farinella (eds.)
Series: Studies in Computational Intelligence 411
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2013

Language: English
Pages: 250
Tags: Computational Intelligence; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision

Front Matter....Pages 1-18
Throwing Down the Visual Intelligence Gauntlet....Pages 1-15
Actionable Information in Vision....Pages 17-48
Learning Binary Hash Codes for Large-Scale Image Search....Pages 49-87
Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition....Pages 89-117
Real-Time Human Pose Recognition in Parts from Single Depth Images....Pages 119-135
Scale-Invariant Vote-Based 3D Recognition and Registration from Point Clouds....Pages 137-162
Multiple Classifier Boosting and Tree-Structured Classifiers....Pages 163-196
Simultaneous Detection and Tracking with Multiple Cameras....Pages 197-214
Applications of Computer Vision to Vehicles: An Extreme Test....Pages 215-250