Authors:
Marzena Bielecka
1
;
Marek Skomorowski
2
and
Andrzej Bielecki
2
Affiliations:
1
Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Poland
;
2
Institute of Computer Science, Jagiellonian University, Poland
Keyword(s):
Syntactic pattern recognition, graph grammars, fuzzy graphs, parallel parsing, robot vision system.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
In syntactic pattern recognition an object is described by symbolic data. The problem of recognition is to determine whether the describing mathematical structure, for instance a graph, belongs to the language generated by a grammar describing the mentioned mathematical structures. So called ETPL(k) graph grammars are a known class of grammars used in pattern recognition. The approach in which ETPL(k) grammars are used was generalized by using probabilistic mechanisms in order to apply the method to recognize distorted patterns. In this paper the next step of the method generalization is proposed. The ETPL(k) grammars are improved by fuzzy sets theory. It turns out that the mentioned probabilistic approach can be regarded as a special case of the proposed one. Applications to robotics are considered as well.