<center> ICCV 2007 <h1> Visual Recognition Tutorial </h1> </center> <hr> <h4>Organisers: </h4> Jiri Matas, CTU Prague, Czech Republic <br> Krystian Mikolajczyk, U. of Surrey, UK <h4> Description: </h4> General, robust, real-time, learnable visual recognition and categorisation for large number of classes is one of the ultimate goals of computer vision. The current state of the art includes methods that fulfill some of the requirements in some restricted domains. For example, methods for real-time recognition of a very large number of locally flat, highly textured, rigid specific objects exist (e.g. Nister & Stewenius CVPR 06); in categorisation the state of the art algorithms can handle objects from tens of (carefully chosen) categories (e.g. Opelt CVPR 06). For some classes, e.g. faces, a robust and fast solution exist in fairly general settings (Viola Jones, ICCV 01). The above-mentioned approaches all have different structure, and as yet no unifying paradigm has emerged. <p> In the tutorial, we will first present the state of the art on selected case studies. The methods will be dissected and their assumption and components identified. In the second part, the key components and algorithms and their will be analysed and compared in depth: <pre> - learning methods, level of supervision - matching and search methods, search in pose space v. correspondence space - object representation: appearance, features-based, edges, holistic - robustness to occlusion, clutter, - indexing methods </pre> In the third part, we will put the current progress into perspective by reviewing "forgotten" problems and listing open problems, such as recognition of objects without surface texture (including the world of polyhedra), of wire-like object where any local patch includes background, of semi-transparent objects etc. <h4> Duration </h4> 3-4 hours <h4> Biographies of organisers </h4> <b> Jiri Matas </b> received the MSc degree in cybernetics (with honours) from the Czech Technical University, Prague, Czech Republic, in 1987 and the PhD degree from the University of Surrey, UK, in 1995. From 1991 to 1997 he was a research fellow at the University of Surrey. In 1997, he joined the Center for Machine Perception at the Czech Technical University in Prague. He has published more than hundred papers in refereed journals and conferences. His work has more than eight hundred citations in the Science Citation Index. He received the best paper prize at the British Machine Vision Conferences in 2002 and 2005. J. Matas has served in various roles at international conferences (e.g. ICCV, CVPR, ICPR, NIPS), co-chairing ECCV 2004 and CVPR 2007. His research interest include object recognition, sequential pattern recognition, invariant feature detection, and Hough Transform and RANSAC-type optimization <p> <b> Krystian Mikolajczyk</b> received the MS degree in electrical engineering from the University of Science and Technology, Cracow, Poland in 1997 and the PhD degree (2002) in computer vision from the Institut National Polytechnique de Grenoble (INPG), France. Dr. Mikolajczyk was a postdoctoral research assistant in the Robotics Research Group of Oxford University in 2002- 2004. He is currently a research assistant at the Technical University of Darmstadt, Germany, and a lecturer at the University of Surrey, United Kingdom. His research interests include invariant feature detection, object and scene recognition, as well as machine learning methods in vision. </body> </html>