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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12128/941
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dc.contributor.authorGdawiec, Krzysztof-
dc.contributor.authorDomańska, Diana-
dc.date.accessioned2018-03-06T09:56:18Z-
dc.date.available2018-03-06T09:56:18Z-
dc.date.issued2011-
dc.identifier.citationInternational Journal of Applied Mathematics and Computer Science 21(4), 757-767, (2011)pl_PL
dc.identifier.issn1641-876X-
dc.identifier.urihttp://hdl.handle.net/20.500.12128/941-
dc.description.abstractOne of the approaches in pattern recognition is the use of fractal geometry. The property of self-similarity of fractals has been used as a feature in several pattern recognition methods. All fractal recognition methods use global analysis of the shape. In this paper we present some drawbacks of these methods and propose fractal local analysis using partitioned iterated function systems with division. Moreover, we introduce a new fractal recognition method based on a dependence graph obtained from the partitioned iterated function system. The proposed method uses local analysis of the shape, which improves the recognition rate. The effectiveness of our method is shown on two test databases. The first one was created by the authors and the second one is the MPEG7 CE-Shape-1 PartB database. The obtained results show that the proposed methodology has led to a significant improvement in the recognition rate.pl_PL
dc.language.isoenpl_PL
dc.rightsUznanie autorstwa-Użycie niekomercyjne-Bez utworów zależnych 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/pl/*
dc.subjectfractalpl_PL
dc.subjectshape recognitionpl_PL
dc.subjectdependence graphpl_PL
dc.titlePartitioned Iterated Function Systems with Division and a Fractal Dependence Graph in Recognition of 2D Shapespl_PL
dc.typeinfo:eu-repo/semantics/articlepl_PL
dc.relation.journalInternational Journal of Applied Mathematics and Computer Sciencepl_PL
dc.identifier.doi10.2478/v10006-011-0060-8-
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