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.
Author(s): Jan J. Koenderink (auth.), Roberto Cipolla, Sebastiano Battiato, Giovanni Maria Farinella (eds.)
Series: Studies in Computational Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2010
Language: English
Pages: 375
Tags: Computational Intelligence; Image Processing and Computer Vision; Artificial Intelligence (incl. Robotics); Signal, Image and Speech Processing
Front Matter....Pages -
Is Human Vision Any Good?....Pages 1-25
Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features for Recognition....Pages 27-49
Dynamic Graph Cuts and Their Applications in Computer Vision....Pages 51-108
Discriminative Graphical Models for Context-Based Classification....Pages 109-134
From the Subspace Methods to the Mutual Subspace Method....Pages 135-156
What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition and Object Categorization....Pages 157-171
Semantic Texton Forests....Pages 173-203
Multi-view Object Categorization and Pose Estimation....Pages 205-231
A Vision-Based Remote Control....Pages 233-262
Multi-view Multi-object Detection and Tracking....Pages 263-280
Shape from Photographs: A Multi-view Stereo Pipeline....Pages 281-311
Practical 3D Reconstruction Based on Photometric Stereo....Pages 313-345
Back Matter....Pages -