Dagstuhl Seminar 08291
Statistical and Geometrical Approaches to Visual Motion Analysis. 14th Workshop “Theoretic Foundations of Computer Vision”
( Jul 13 – Jul 18, 2008 )
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Organizers
- Daniel Cremers (Universität Bonn, DE)
- Bodo Rosenhahn (Leibniz Universität Hannover, DE)
- Alan L. Yuille (UCLA, US)
Contact
Press/News
Press Reviews
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Wie man Bewegungen analysiert
Interview Wolfgang Back vom Computerclub Zwei mit Prof. Dr. Daniel Cremers (Universität Bonn) und Prof. Dr. Bodo Rosenhahn (MPII Saarbrücken; Folge 118, 21.07.08 - Download der Sendung 118, 32 Kbit/s (~7 MB)
Motion analysis is central to both human and machine vision. It involves the interpretation of image data over time. It is crucial for a range of motion tasks such as obstacle detection, depth estimation, video analysis, scene interpretation, video compression and other applications. Motion analysis is difficult because it requires modeling the complicated relationships between the observed image data and the motion of objects and motion patterns (e.g. falling rain) in the visual scene.
This workshop was focused on critical aspects of motion analysis, including motion segmentation and the modeling of motion patterns. The aim was to gather researchers who are experts in the different motion tasks and in the different techniques used. These techniques include variational approaches, level set methods, probabilistic models, graph cut approaches, factorization techniques, and neural networks. All these techniques can be subsumed within statistical and geometrical frameworks.
We also involved experts in the study of human and primate vision. Primate visual systems are extremely sophisticated at processing motion so there is much to be learnt from studying them. In particular, we wanted to relate the computational models of primate visual systems to those developed for machine vision. Here, several researchers from the cognitive sciences and biologically inspired vision rounded off the overall high quality of presentations and discussions.
Another important component of the workshop was to develop datasets of image sequences with the associated motion ground truth. These datasets can be used as benchmarks to compare the performance of motion analysis models. They can also be used as data to train statistical models of motion analysis. Datasets with ground truth are being increasingly used in other aspects of machine vision but, at present, there are only very limited motion datasets (e.g. the Yosemite sequence). Here we would like to point out the empeda test sequences, available at http://www.citr.auckland.ac.nz/6D/ or several discussions about the current optic flow benchmark at middlebury http://vision.middlebury.edu/flow/
We made the seminar interactive with plenty of time for discussion. The participants were also encouraged to exchange different modeling techniques and research experiences. We have also identified outstanding unsolved problems in motion analysis and discussed them during highly active group meetings. The participants of this seminar enjoyed the atmosphere and the services at Dagstuhl very much. The quality of this center is unique. It was also a pleasure for the organizers to be involved in a radio interview of the extit{Computer Club Zwei}.
There will be an edited book (within Springer's series on Lecture Notes of Computer Science, LNCS) following the seminar, and all seminar participants have been invited to contribute with chapters. The deadline for those submissions is in November 2008 (allowing to incorporate results or ideas stimulated by the seminar), and submissions will be reviewed (as normal). Expected publication date is 2009.
- Christophe Avenel (INRIA Rennes - Bretagne Atlantique, FR)
- Adrian Barbu (Florida State University, US)
- John L. Barron (University of Western Ontario - London, CA)
- Benjamin Berkels (Universität Bonn, DE) [dblp]
- Yuri Boykov (University of Western Ontario, CA) [dblp]
- Thomas Brox (TU Dresden, DE) [dblp]
- Andrés Bruhn (Universität des Saarlandes, DE) [dblp]
- Daniel Cremers (Universität Bonn, DE) [dblp]
- Andrew Delong (University of Western Ontario - London, CA)
- Gianfranco Doretto (General Electric - Niskayuna, US)
- Dirk Farin (Robert Bosch GmbH - Hildesheim, DE)
- Vittorio Ferrari (ETH Zürich, CH) [dblp]
- Christoph S. Garbe (Universität Heidelberg, DE)
- Martin A. Giese (Universitätsklinikum Tübingen, DE)
- Andrew Glennerster (University of Reading, GB)
- Norberto Grzywacz (USC - Los Angeles, US)
- Christoph Gütter (Siemens - Princeton, US)
- Simon Hermann (University of Auckland, NZ)
- Vaclav Hlavac (Czech Technical University, CZ) [dblp]
- David C. Hogg (University of Leeds, GB) [dblp]
- Atsushi Imiya (Chiba University, JP) [dblp]
- Reinhard Klette (University of Auckland, NZ) [dblp]
- Timo Kohlberger (Siemens - Princeton, US)
- Vladimir Kolmogorov (Univ. College London, GB) [dblp]
- Norbert Krüger (University of Southern Denmark - Odense, DK) [dblp]
- Volker Krüger (Copenhagen Inst. of Technology Aalborg University, DK)
- Hanspeter Mallot (Universität Tübingen, DE)
- Rudolf Mester (Goethe-Universität - Frankfurt a. M., DE)
- Tal Nir (Technion - Haifa, IL)
- Thomas Pock (Universität Bonn, DE) [dblp]
- Bodo Rosenhahn (Leibniz Universität Hannover, DE) [dblp]
- Stefan Roth (TU Darmstadt, DE) [dblp]
- Martin Rumpf (Universität Bonn, DE) [dblp]
- Hanno Scharr (Forschungszentrum Jülich, DE)
- Christian Schmaltz (Universität des Saarlandes, DE)
- Christoph Schnörr (Universität Heidelberg, DE)
- Thomas Schoenemann (Universität Bonn, DE)
- Tobi Vaudrey (University of Auckland, NZ)
- Andreas Wedel (Daimler AG - Sindelfingen, DE)
- Lennart Wietzke (Christian-Albrecht-Universität zu Kiel, DE)
- Alan L. Yuille (UCLA, US) [dblp]
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Classification
- computer graphics / computer vision
Keywords
- Motion Analysis
- Statistical Methods
- Computational Geometry
