Objetivo The project PRINCESS pursues scientific collaboration between six groups. The project is supposed to run for 24 months starting from approximately March 2005.The scientific objective of the PRINCESS project is to contribute to theoretical basis for creating algorithmic technologies of pattern recognition and supervised and unsupervised learning. The emphasis will be given to object recognition from signals, symbolic sequences and images. This technology is needed for sharing knowledge and uncertain information in informatics in general. It is applicable in mobile robotics and in systems with distributed sensors as well.The proposal addresses both topics expected in the call, i.e.(a) foundations and methodologies for excerpting knowledge from uncertain (including sensorial) information and(b) specifically from images and video.The traditional (and in a certain sense problematic) approach is to extract such a kind of informative characteristics from data, which represent each object in a vector feature space. There exists a variety of heuristic ways to evaluate the dissimilarity between signals, symbolic sequences or images using an appropriate deformation of their argument axes in the vector space and to form set of two-argument functions each of which possess all the properties of a metric. There have been attempts to utilize structure of signals, symbolic sequences and images to enhance recognition capabilities. This projects aims in this direction too. A structural approach implies inferring the final decision from descriptions of parts and expressed relationships among them. The idea of the proposed investigation is to introduce a collection of heuristically chosen metrics. They will be used for arranging a cooperation of the respective compactness hypotheses to achieve better predictive properties of the decision rule inferred from a small training set by statistical as well as structural techniques.The first objective is to create a general theoretical and methodological framework in the following areas:(1) finding empirically regularities in sets of signals, symbolic sequences and images,(2) dissimilarity-based structural pattern recognition,(3) recognition using two-dimensional context-free languages.More specifically the following topics are to be investigated:(a) Metrics for signals, symbolic sequences and images.(b) Dissimilarity-based pattern recognition using structured stochastic models.(c) Fusion of information represented by various dissimilarity metrics.(d) 2D context free languages for pattern recognition.The second objective is to transform theoretical knowledge into operational skills. This is going to be performed through conducting pilot applications at UniS Guildford (human faces), at TU Dresden (utilization of structured stochastic models to 3D reconstruction from 2D images) and at CTU Prague (structured printed 2D documents of mathematical formulae). Programa(s) IC-INTAS - International Association for the promotion of cooperation with scientists from the independent states of the former Soviet Union (INTAS), 1993- Tema(s) IT 2004 - Thematic Call - Information Technology Convocatoria de propuestas Data not available Régimen de financiación Data not available Coordinador Czech Technical University in Prague Aportación de la UE Sin datos Dirección Technicka 2 16627 Prague 2 Chequia Ver en el mapa Coste total Sin datos Participantes (5) Ordenar alfabéticamente Ordenar por aportación de la UE Ampliar todo Contraer todo Russian Academy of Sciences, Institute Scientific Council "Cybernetics" of the RAS Rusia Aportación de la UE Sin datos Dirección Vavilov St. 40 117968 Moscow Ver en el mapa Coste total Sin datos Technische Universitat Dresden Alemania Aportación de la UE Sin datos Dirección Mommsenstrasse 13 D-01062 Dresden Ver en el mapa Coste total Sin datos Tula State University Rusia Aportación de la UE Sin datos Dirección Lenin Ave. 92 300600 Tula Ver en el mapa Coste total Sin datos Ukrainian Academy of Sciences, International Research and Training Centre for Information Technologies and Systems from the NASU Ucrania Aportación de la UE Sin datos Dirección Prospect Akademika Glushkova 40 03680 Kiev Ver en el mapa Coste total Sin datos University of Surrey Reino Unido Aportación de la UE Sin datos Dirección The Stag Hill GU2 7XH Guildford Ver en el mapa Coste total Sin datos
