Variational, Geometric, and Level Set Methods in Computer Vision Thi
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 throug
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Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos New York University, NY, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany
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Nikos Paragios Olivier Faugeras Tony Chan Christoph Schnörr (Eds.)
Variational, Geometric, and Level Set Methods in Computer Vision Third International Workshop, VLSM 2005 Beijing, China, October 16, 2005 Proceedings
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Volume Editors Nikos Paragios C.E.R.T.I.S. Ecole Nationale des Ponts et Chaussées Champs sur Marne, France E-mail: [email protected] Olivier Faugeras I.N.R.I.A 2004 route des lucioles, 06902 Sophia-Antipolis, France E-mail: [email protected] Tony Chan University of California at Los Angeles Department of Mathematics Los Angeles, USA E-mail: [email protected] Christoph Schnörr University of Mannheim Department of Mathematics and Computer Science, Germany E-mail: [email protected]
Library of Congress Control Number: Applied for CR Subject Classification (1998): I.4, I.5, I.3.5, I.2.10, I.2.6, F.2.2 ISSN ISBN-10 ISBN-13
0302-9743 3-540-29348-5 Springer Berlin Heidelberg New York 978-3-540-29348-4 Springer Berlin Heidelberg New York
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Preface
Mathematical methods has been a dominant research path in computational vision leading to a number of areas like filtering, segmentation, motion analysis and stereo reconstruction. Within such a branch visual perception tasks can either be addressed through the introduction of application-driven geometric flows or through the minimization of problem-drive
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