CALL FOR PAPERS +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + ECAI 2000 Workshop on + + Machine Learning in Computer Vision + + + + Tuesday, 22nd August 2000 + +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ before 14th Biennial European Conference on Artificial Intelligence Humboldt University, Berlin http://www.ecai2000.hu-berlin.de/ Technical Description Learning is one of the next challenging frontiers for computer vision research, and it has been receiving increasing attention in the recent years. This workshop will provide a forum for discussing current research in AI and pattern recognition that pertains to machine learning in computer vision systems. >From the standpoint of computer vision systems, machine learning can offer effective methods for automating the acquisition of visual models, adapting task parameters and representation, transforming signals to symbols, building trainable image processing systems, focusing attention on target object. To develop successful applications, however, we need to address the following issues: - How is machine learning used in current computer vision systems? - What are the models of a computer vision system that might be learned rather than hand-crafted by the designer? - What machine learning paradigms and strategies are appropriate to the computer vision domain? - How do we represent visual information? - How does machine learning help to transfer the experience gained in creating a vision system in one application domain to a vision system for another domain? >From the standpoint of machine learning systems, computer vision can provide interesting and challenging problems. Many studies in machine learning assume that a careful trainer provides internal representations of the observed environment, thus paying little attention to the problems of perception. Unfortunately, this assumption leads to the development of brittle systems with noisy, excessively detailed or quite coarse descriptions of the perceived environment. Some specific machine learning research issues raised by the computer vision domain are: - How dealing with noisy observations? - How can large sets of images with no annotation be used for learning? - How dealing with mutual dependency of visual concepts? - What are the criteria for evaluating the quality of learning processes in computer vision systems? - When a computer vision system should start/stop the learning process and/or revise acquired models? - When is it useful to adopt several representations of the perceived environment with different levels of abstraction? The workshop is aimed to be a high communicative meeting place for researchers working on similar topics, but from different communities. In order to achieve these goals, workshop will consist of one or two invited talks, followed by short presentations and longer discussions. Each author will be encouraged to read another accepted paper and to comment on it after the original talk was given. All ECAI'2000-MLCV workshop participants must register both for the main ECAI'2000 conference and the workshop itself. Workshop attendance will be limited to registered participants. Topics The workshop will maintain a balance between theoretical issues and descriptions of implemented systems to promote synergy between theory and practice. Works in areas such as statistical pattern recognition are also welcome. Topics of interest include, but are not limited to: - Learning to Recognize Shapes - Supervised Learning of visual models - Unsupervised Learning for structure detection in images - Multistrategy Learning in Vision - Learning and Refining Visual Models - Multi-level Learning and Reuse of Learned Concepts - Learning Important Features for Image Analysis - Relational Learning in Vision - Context in Visual Learning - Image segmentation via learning - Probabilistic model estimation and selection - Applications such as medical imaging, object recognition, remote sensing, digital maps, document image analysis and recognition, spatial reasoning Submission Procedure Authors are invited to submit original research contributions or experience reports in English. Submitted papers must be unpublished and substantially different from papers under review. Papers that have been or will be presented at small workshops/symposia whose proceedings are available only to the attendees may be submitted. Papers should be no longer than 5000 words (10 pages, approximately). Papers should be sent electronically (postscript or pdf) not later than March 20th, 2000 to Donato Malerba Papers will be selected on the basis of review of full paper contributions. Authors should make certain that the learning techniques they describe address the special issues that are associated with problems in computer vision. Final camera-ready copies of accepted papers will be due by June 1st, 2000. Important Dates - Deadline for papers: March 20th, 2000 - Notification of acceptance: May 1st, 2000 - Camera-ready copies of papers: June 1st, 2000 - Workshop on ECAI-98: August 22nd, 2000 Organizing Committee This workshop will be organized by the following people: - Joachim M. Buhmann, University of Bonn, Germany - Terry Caelli, The University of Alberta, Alberta, Canada - Floriana Esposito, University of Bari, Italy (cochair) - Donato Malerba, University of Bari, Italy (cochair) - Maria Petrou, University of Surrey, UK - Petra Perner, Institute of Computer Vision and Applied CS, Leipzig, Germany - Tomaso A. Poggio, MIT, Boston, MA - Alessandro Verri, University of Genoa, Italy - Tatjana Zrimec, University of Ljubljana, Slovenia Workshop Home Page: http://www.di.uniba.it/~malerba/ws-ecai2000/ --------------------------------------------------- Prof. Donato MALERBA Dipartimento di Informatica Tel: +39 - 080 5443269 Universita' degli Studi Fax: +39 - 080 5443196 via Orabona, 4 email:malerba@di.uniba.it Bari I-74125 Italy http://www.di.uniba.it/~malerba ---------------------------------------------------