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Multi-threshold Segmentation Using Learning Automata

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Engineering Applications of Soft Computing

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 129))

Abstract

Multi-threshold selection for image segmentation is considered as a critical pre-processing step for image analysis, pattern recognition and computer vision. This chapter explores the use of the Learning Automata (LA) algorithm to compute the thresholding points for segmentation proposes. LA is a heuristic method which is able to solve complex optimization problems with interesting results in parameter estimation. Different to other optimization approaches, LA explores in the probability space providing appropriate convergence properties and robustness. In this chapter the segmentation task is considered as an optimization problem and the LA is used to generate the image multi-threshold points. In this approach, one 1-D histogram of a given image is approximated through a Gaussian mixture model whose parameters are calculated using the LA algorithm. Each Gaussian function approximating the histogram represents a pixel class and therefore a thresholding point. Experimental results show fast convergence of the method, avoiding the typical sensitivity to initial conditions.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany

    Margarita-Arimatea Díaz-Cortés & Raúl Rojas

  2. CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico

    Erik Cuevas

Authors
  1. Margarita-Arimatea Díaz-Cortés
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  2. Erik Cuevas
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  3. Raúl Rojas
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Correspondence to Margarita-Arimatea Díaz-Cortés .

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Díaz-Cortés, MA., Cuevas, E., Rojas, R. (2017). Multi-threshold Segmentation Using Learning Automata. In: Engineering Applications of Soft Computing. Intelligent Systems Reference Library, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-319-57813-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-57813-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57812-5

  • Online ISBN: 978-3-319-57813-2

  • eBook Packages: EngineeringEngineering (R0)

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