Is Human Vision Any Good? -- Knowing a Good Feature When You See It: Ground Truth and Methodology to Evaluate Local Features for Recognition -- Dynamic Graph Cuts and Their Applications in Computer Vision -- Discriminative Graphical Models for Context-Based Classification -- From the Subspace Methods to the Mutual Subspace Method -- What, Where and Who? Telling the Story of an Image by Activity Classification, Scene Recognition and Object Categorization -- Semantic Texton Forests -- Multi-view Object Categorization and Pose Estimation -- A Vision-Based Remote Control -- Multi-view Multi-object Detection and Tracking -- Shape from Photographs: A Multi-view Stereo Pipeline -- Practical 3D Reconstruction Based on Photometric Stereo.

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.