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1
Content available remote Inference of Parsable Graph Grammars for Syntactic Pattern Recognition
Flasiński M.
Fundamenta Informaticae
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2007
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Vol. 80, nr 4
379-413
EN
A research into a syntactic pattern recognition model based on ( edNLC) graph grammars (introduced and investigated in Janssens and Rozenberg Inform. Sci. 20 (1980), 191-216, and Janssens, Rozenberg and Verraedt Comp. Vis. Graph. Image Process. 18 (1982), 279-304) has resulted in defining the efficient, O(n2), parsing schemes for the ETPL(k) subclass of these grammars and applying it for scene analysis, CAD/CAM object analysis and constructing AI systems (Flasiński Patt. Recogn. 21 (1988), 623-629, Flasiński Comp. Vis. Graph. Image Process. 47 (1989), 1-21, Flasiński Patt. Recogn. 26 (1993), 1-16, Flasiński Comp. Aided-Des. 27 (1995), 403-433, Flasiński Theor. Comp. Sci. 201 (1998), 189-231). In the paper the grammatical inference method for the parsable ETPL(k) graph grammars is defined, completing the development of this syntactic pattern recognition model.
2
Content available remote Distorted pattern recognition and analysis with the help of IE(f] graph representation
Sędziwy A.
Computer Science
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2002
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Vol. 4
21-37
EN
An algorithm for distorted pattern recognition is presented. It's generalization of M. Flasiński results (Pattern Recognition, 27, 1-16, 1992). A new formalism allows to make both qualitative and quantitive distortion analysis. It also enlarges parser flexibility by extending the set of patterns which may be recognized.
PL
Praca zawiera algorytm syntaktycznego rozpoznawania obrazów rozmytych (zniekształconych), reprezentowanych przez IE(f) grafy. Jest on uogólnieniem algorytmu parsera dla gramatyk ETPL(k), podanego przez M. Flasińskiego dla obrazów zniekształconych. Zaproponowany formalizm pozwala na ilościową i jakościową analizę rozmycia badanego obiektu.
3
Content available remote Parsing of random graphs for scene analysis
Skomorowski M.
Machine Graphics and Vision
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1998
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Vol. 7, No. 1/2
313-323
EN
Further results of research into parsing of random graphs for recognition of distored scenes ([9, 11]) are presented. An efficient top-down parallel parsing algorithm for analysis of distored scenes is proposed. The proposed approach involves parsing of graph grammars. To take into acount all variations of a distored scene under study, a probabilistic description of the scene is needed. The random graph approach ([9, 11]) is proposed here for such a description.
4
Content available remote Parsing of random graph languages for automated inspection in statisfical-based quality assurance systems
Flasiński M., Skomorowski M.
Machine Graphics and Vision
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1998
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Vol. 7, No. 3
565-623
EN
Basic requirements for an automated visual inspection in intelligent SPC-oriented quality assurance systems are discussed. Extensions of feature IE-graphs representing solids in CAD ([62]) to a stochastic model of manufacturing processes are proposed. An efficient random graph language analysis based on parsable ETPL(k) graph grammars([55)] is presented as a tool for intelligent reasoning in high layer modules of automated inspection systems. The first applications of the model are shown.
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