Olivier Faugeras - Böcker

Mathematical methods has been a dominant research path in computational vision leading to a number of areas like ?ltering, segmentation, motion analysis and stereo reconstruction. Within such a branch visual perception tasks can either be addressed through the introduction of application-driven geometric ?ows or through the minimization of problem-driven cost functions where their lowest potential corresponds to image understanding. The 3rd IEEE Workshop on Variational, Geometric and Level Set Methods focused on these novel mathematical techniques and their applications to c- puter vision problems. To this end, from a substantial number of submissions, 30 high-quality papers were selected after a fully blind review process covering a large spectrum of computer-aided visual understanding of the environment. The papers are organized into four thematic areas: (i) Image Filtering and Reconstruction, (ii) Segmentation and Grouping, (iii) Registration and Motion Analysis and (iiii) 3D and Reconstruction. In the ?rst area solutions to image enhancement, inpainting and compression are presented, while more advanced applications like model-free and model-based segmentation are presented in the segmentation area. Registration of curves and images as well as multi-frame segmentation and tracking are part of the motion understanding track, while - troducing computationalprocessesinmanifolds,shapefromshading,calibration and stereo reconstruction are part of the 3D track. We hope that the material presented in the proceedings exceeds your exp- tations and will in?uence your research directions in the future. We would like to acknowledge the support of the Imaging and Visualization Department of Siemens Corporate Research for sponsoring the Best Student Paper Award.

A collection of papers on computer vision research in Euro-pe, with sections on image features, stereo and reconstruc-tion, optical flow, motion, structure from motion, tracking,stereo and motion, features and shape, shape description,and recognition and matching.

This volume treats the analysis of 3D dynamic scenes using a stereovision system. Several approaches are described, for example two different methods of dealing with long and short sequences of images of an unknown environment, including an arbitrary number of rigid mobile objects. Results obtained from stereovision systems are found to be superior to those from monocular image systems, which are often very sensitive to noise and therefore of little use in practice. It is shown that motion estimation can be further improved by the explicit modelling of uncertainty in geometric objects. The techniques developed in this book have been successfully demonstrated with a large number of real images in the context of visual navigation of a mobile robot.