© 2005

Variational, Geometric, and Level Set Methods in Computer Vision

Third International Workshop, VLSM 2005, Beijing, China, October 16, 2005. Proceedings

  • Nikos Paragios
  • Olivier Faugeras
  • Tony Chan
  • Christoph Schnörr
Conference proceedings VLSM 2005

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3752)

Table of contents

  1. Front Matter
  2. Jing Yuan, Christoph Schnörr, Gabriele Steidl, Florian Becker
    Pages 1-12
  3. Y. Gur, N. Sochen
    Pages 13-24
  4. Martin Burger, Stanley Osher, Jinjun Xu, Guy Gilboa
    Pages 25-36
  5. Irena Galić, Joachim Weickert, Martin Welk, Andrés Bruhn, Alexander Belyaev, Hans-Peter Seidel
    Pages 37-48
  6. Leah Bar, Alexander Brook, Nir Sochen, Nahum Kiryati
    Pages 49-60
  7. Yan Niu, Tim Poston
    Pages 61-72
  8. Jean-François Aujol, Guy Gilboa, Tony Chan, Stanley Osher
    Pages 85-96
  9. François Lauze, Mads Nielsen
    Pages 97-108
  10. Ganesh Sundaramoorthi, Anthony Yezzi, Andrea Mennucci
    Pages 109-120
  11. Mariano Rivera, Omar Ocegueda, Jose L. Marroquin
    Pages 137-148
  12. Jean-François Aujol, Tony Chan
    Pages 161-172
  13. Gabriel Peyré, Laurent Cohen
    Pages 173-185
  14. Olivier Juan, Renaud Keriven
    Pages 186-197
  15. Maxime Taron, Nikos Paragios, Marie-Pierre Jolly
    Pages 198-209
  16. Sheshadri R. Thiruvenkadam, David Groisser, Yunmei Chen
    Pages 222-234

About these proceedings


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.


3D modeling 3D vision Active contour Computer Vision Image segmentation Mapping Shading Stereo Textur classifier systems image analysis image enhancement motion analysis stereo vision topology

Editors and affiliations

  • Nikos Paragios
    • 1
  • Olivier Faugeras
    • 2
  • Tony Chan
    • 3
  • Christoph Schnörr
    • 4
  1. 1.MAS - Ecole Centrale Paris, Grande Voie des VignesChatenay-MalabryFrance
  2. 2.I.N.R.I.A, 2004 route des lucioles,Sophia-AntipolisFrance
  3. 3.Department of MathematicsUCLA 
  4. 4.Image and Pattern Analysis Group, Heidelberg Collaboratory for Image ProcessingUniversity of HeidelbergGermany

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