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.
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Bielecka, M., Bielecki, A. (2012). An Evolutionary-Neural Algorithm for Solving Inverse IFS Problem for Images in Two-Dimensional Space. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2012. Lecture Notes in Computer Science, vol 7594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33564-8_3
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DOI: https://doi.org/10.1007/978-3-642-33564-8_3
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