CALL FOR PAPERS
 
 SECOND WORKSHOP ON STATISTICAL METHODS IN VIDEO PROCESSING
 in conjunction with 
 European Conference on  Computer Vision
 
 Prague, Czech Republic, May 16, 2004
 
 Workshop web-site:  http://www.scr.siemens.com/smvp04/
 
 SCOPE
 In recent years a variety of advanced statistical methods became
 standard tools for processing visual information. Our current
 understanding of the performance of these techniques when applied to
 long video sequences is, however, rather limited. There are at least two
 reasons for this. First, the theoretical performance bounds are most
 often computed with limiting assumptions, which are not valid in
 practice. Second, robust vision systems require not only superior
 analysis to handle outliers and model selection, but also strategies to
 adapt to the non-stationary behavior of the input. 
 
 This workshop focuses on recent progress in the application of modern
 statistics to solve computer vision tasks that use non-stationary video
 data. For such sequences, the underlying models and parameters of the
 algorithms have to be often adapted or reinitialized in time, according
 to higher-level strategies. 
 
 Examples are sequences containing sudden or gradual changes in the input
 data statistics, such as: a walking/running/turning person;
 empty/crowded train stations; arrival of a train/metro; turning on/off
 (either abruptly or gradually) the light source(s); transition from day
 to night; changing the environment from indoor to outdoor; video signal
 captured from a vehicle entering a tunnel; weather changing from clear
 sky to rain/snow; dynamic occlusions; time-varying patterns (medical
 perfusion), summer/winter surveillance, etc. 
 
 The workshop aims at bringing together researchers with various
 backgrounds, interested in building robust vision algorithms that can
 accommodate changes in the input statistics. Areas of interest include,
 but are not limited to:
 o	Robust Statistical Techniques
 o	Tracking and Motion Analysis
 o	Stereo and Structure from Motion
 o	2D & 3D Scene Analysis
 o	Fluid Motion Analysis
 o	Dynamic Background Modeling
 o	Dynamic Texture Analysis 	
 o	Vision-based Driver Assistance
 o	Video Segmentation and Indexing 
 o	Gesture Recognition
 o	Spatio-Temporal Feature Selection
 o	Applications in Medical Image Analysis
 o	Applications in Meteorological Imagery
 o	Biometrics and Surveillance
 
 This event is a sequel of the 1st Workshop on Statistical Methods in
 Video Processing, organized in conjunction with the 7th European
 Conference in Computer Vision, 2002. 
 
 BEST STUDENT PAPER PRIZE
 Siemens Corporate Research will sponsor a prize awarded to the best student
 paper. 
 
 IMPORTANT DATES
 Submission deadline:              February 1, 2004  
 Author notification:                March 15, 2004 
 Workshop:                             May 16, 2004 
 
 PAPER SUBMISSION AND REVIEW 
 The submission is electronic, pdf file. The paper should be in English, no
 longer than 12 pages in Springer LNCS format (same format as ECCV 2004). The
 review is double blind, please do not identify the author(s) in the
 submission. The workshop proceedings will be published in the Springer
 Lecture Notes in Computer Science (LNCS). The following link should be used
 for paper submission:
 http://www.ds.eng.monash.edu.au/smvp/submission/submission_smvp2.html
 
 ORGANIZING COMMITTEE
 Dorin Comaniciu,  Siemens Corporate Research, USA 
 Kenichi Kanatani, Okayama University, Japan 
 Rudolf Mester, Goethe-Universitaet, Germany 
 David Suter, Monash University, Australia 
 
 PROGRAM COMMITTEE
 Bir Bhanu, University of California, USA 
 Patrick Bouthemy, IRISA / INRIA, France 
 Mike Brooks, University of Adelaide, Australia 
 Yaron Caspi, Weizmann Institute of Science, Israel 
 Rama Chellappa, University of Maryland, USA 
 Andrew Fitzgibbon, Oxford University, United Kingdom 
 Radu Horaud, INRIA, France 
 Naoyuki Ichimura, AIST / Columbia University, Japan 
 Michael Isard, Microsoft Research, USA 
 Bogdan Matei, Sarnoff Corporation, USA 
 Takashi Matsuyama, Kyoto University, Japan 
 Visvanathan Ramesh, Siemens Corporate Research, USA 
 Harpreet Sawhney, Sarnoff Corporation, USA 
 Stuart Schwartz, Princeton University, USA 
 Mubarak Shah, University of Central Florida, USA 
 Nobutaka Shimada, Osaka University, Japan 
 Zhengyou Zhang, Microsoft Research, USA 
 Ying Zhu, Siemens Corporate Research, USA