Computer Vision: Detection, Recognition and Reconstruction
Roberto Cipolla, Sebastiano Battiato, Giovanni Maria Farinella
Springer Science & Business Media, May 11, 2010 - Computers - 375 pages
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 first two editions of the school on topics such as Recognition, Registration and Reconstruction. The chapters provide an in-depth overview of these challenging areas with key references to the existing literature.
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albedo algorithm augmenting path camera canonical classiﬁer color Computer Vision Conditional Random Fields Conference on Computer conﬁdence conﬁguration constraints corresponding dataset deﬁned depth depth-map descriptors detector dynamic ECCV edge efﬁcient energy function Equation evaluation Fei-Fei ﬁeld Figure ﬁlter ﬁnal ﬁnd ﬁrst ﬂow frame geometry graph cuts Heidelberg histogram homography illumination image segmentation inference interaction International Conference labels latent variable layer learning LNCS machine vision matching min-marginal minimizing motion multi-view stereo node object category object class object recognition observer obtained occlusion optimal parameters patch Pattern Recognition performance photo-consistency photometric stereo pixel pose estimation problem Proceedings random RANSAC reconstruction regions regularisation residual graph robust scene search trees semantic texton sensor sequence shown shows similar solution speciﬁc Springer st-mincut Subspace Subspace Method surface tion tracker tracking transformations vector Vision and Pattern visual hull voxels