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.
| ISBN: | 9783540293484 |
| Publication date: | 4th October 2005 |
| Author: | Nikos Paragios, Olivier Faugeras, Tony Chan, Christoph Schnoerr |
| Publisher: | Springer an imprint of Springer Berlin Heidelberg |
| Format: | Paperback |
| Pagination: | 372 pages |
| Series: | Lecture Notes in Computer Science |
| Genres: |
Computer vision Pattern recognition Image processing Algorithms and data structures Graphics programming Artificial intelligence |
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.
Variational, Geometric, and Level Set Methods in Computer Vision Image Processing, Computer Vision, Pattern Recognition, and Graphics features in the following genres: Computer vision, Pattern recognition, Image processing, Algorithms and data structures, Graphics programming, Artificial intelligence
Variational, Geometric, and Level Set Methods in Computer Vision Image Processing, Computer Vision, Pattern Recognition, and Graphics is available in Paperback
Variational, Geometric, and Level Set Methods in Computer Vision Image Processing, Computer Vision, Pattern Recognition, and Graphics was written by Nikos Paragios, Olivier Faugeras, Tony Chan, Christoph Schnoerr and published by Springer an imprint of Springer Berlin Heidelberg
Variational, Geometric, and Level Set Methods in Computer Vision Image Processing, Computer Vision, Pattern Recognition, and Graphics has 372 pages
Yes it is part of Lecture Notes in Computer Science series