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International Journal of Applied Mathematics and Computer Science

Journal of University of Zielona Gora and Lubuskie Scientific Society

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Volume 21, Issue 4


Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Krzysztof Gdawiec
  • Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Diana Domańska
  • Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2011-12-21 | DOI: https://doi.org/10.2478/v10006-011-0060-8

Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

One 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.

Keywords: fractal; partitioned iterated function system; shape recognition; dependence graph

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About the article

Published Online: 2011-12-21

Published in Print: 2011-12-01

Citation Information: International Journal of Applied Mathematics and Computer Science, Volume 21, Issue 4, Pages 757–767, ISSN (Print) 1641-876X, DOI: https://doi.org/10.2478/v10006-011-0060-8.

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