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An Evolutionary-Neural Algorithm for Solving Inverse IFS Problem for Images in Two-Dimensional Space

  • Marzena Bielecka
  • Andrzej Bielecki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7594)

Abstract

In this paper an approach based on hybrid, evolutionary-neural computations to the IFS inverse problem is presented. Having a bitmap image we look for an IFS having the attractor approximating of a given image with a good accuracy. A method using IFSes consisting of a variable number of mappings is proposed. A genom has hierarchical structure. A number of different operators acting on various levels of the genome are introduced. The algorithm described in [7] is aided by multi-layer neural networks. Such improved algorithm is less time consuming.

Keywords

Contractive Mapping Iterate Function System Proportional Selection Contractive Factor Point Coverage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marzena Bielecka
    • 1
  • Andrzej Bielecki
    • 2
  1. 1.Chair of Geoinformatics and Applied Computer ScienceAGH University of Science and TechnologyKrakówPoland
  2. 2.Institute of Computer ScienceJagiellonian UniversityKrakówPoland

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