WORKSHOP ON LEARNING IN COMPUTER VISION in conjunction with ECCV '98 June 6, 1998 Freiburg, Germany Description ----------- In recent years rising computer performance has made it possible to exploit complex statistical models and to learn and estimate their parameters from an increasing amount of data. Therefore the issues of computational and statistical learning theory and Bayesian inference become more and more relevant for computer vision applications. Especially the related topics of generalization and choice of model complexity are of central importance in computer vision. Furthermore, the question of needed accuracy for optimization and parameter estimation turns out to be a closely related topic. The application of methods from statistical learning theory and neurally inspired approaches in computer vision are rather diverse and learning in computer vision is by no means a homogeneous field. But the necessity becomes more and more evident to take a more fundamental point of view and to clarify the multiple implications that the recent achievements of statistical learning theory have on computer vision problems. Statistical learning theory might have significant influence on many applications ranging from classification and statistical object recognition, grouping and segmentation to statistical field models and optimization. We are convinced that focussing on these joint aspects may yield a major contribution to the understanding and improvement of the diverse range of learning applications in computer vision. A workshop on learning in computer vision may greatly contribute to these goals. Workshop Issues --------------- The workshop will focus on the latest developments of learning in computer vision and will try to clarify to what extent statistical learning theory and Bayesian inference support computer vision applications. The workshop will present high quality oral contributions on any aspects of learning in computer vision, including but not restricted to the following topics: * Supervised Learning and its application to classification, support vector networks and model learning * Unsupervised Learning for structure detection in images * Robustness of Computer Vision algorithms and generalization * Probabilistic model estimation and selection, e.g. Bayesian inference for vision Attendance and Workshop Format ------------------------------ The workshop will consist of invited keynote talks and regular talks in one track. For submissions please send an extended abstract of 1-2 pages by March 31, 1998 to Workshop Learning in Computer Vision c/o Prof. Joachim Buhmann Institut fuer Informatik Roemerstrasse 164 D-53117 Bonn Germany In case of more submissions than available time slots a selection will be made based on a peer review of the submissions by the program committee. Venue ----- The workshop will be held in Freiburg, Germany on June 6, 1998 in conjunction with the European Conference on Computer Vision (ECCV '98). Program Committee ----------------- * Joachim M. Buhmann, Chair (University of Bonn, Germany) * Andrew Blake (University of Oxford, UK) * Jitendra Malik (UC Berkeley, USA) * Tomaso Poggio (MIT, USA) * Daphna Weinshall (Hebrew University, Israel) Local Organization: Andreas Polzer, Jan Puzicha (University of Bonn) To obtain further information please contact: WWW: http://www-dbv.cs.uni-bonn.de/learning.html e-mail: jan@cs.uni-bonn.de