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Multi-level Image Thresholding Segmentation Using 2D Histogram Non-local Means and Metaheuristics Algorithms

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Applications of Hybrid Metaheuristic Algorithms for Image Processing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 890))

Abstract

One of the goals for the multi-level image thresholding segmentation is to divide the image into several homogeneous regions without overlap**. The performance of segmentation approaches when are used 1D histogram-based methods are unsatisfactory as they consider the gray level of an image only and do not deal with spatial correlation among the pixels. The alternative is to use a 2D histogram that permits to handle the situations described above. This chapter explains the use of PSO and SCA metaheuristics algorithms to find the best thresholds for images segmentation, using the two-dimensional (2D) histogram non-local means and Rényi entropy as an objective function. To compare the performance of the results it uses the method 2DNLMeKGSA propose by H. Mittal and M. Saraswat. The methods have tested on five images from the Berkeley Segmentation Dataset and Benchmark (BSDS300) in terms of subjective and objective evaluations.

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Authors and Affiliations

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

    Andrea A. Hernandez del Rio, Erik Cuevas & Daniel Zaldivar

Authors
  1. Andrea A. Hernandez del Rio
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  2. Erik Cuevas
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  3. Daniel Zaldivar
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Corresponding author

Correspondence to Andrea A. Hernandez del Rio .

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Editors and Affiliations

  1. CUCEI, University of Guadalajara, Guadajalara, Mexico

    Diego Oliva

  2. CUCEI, University of Guadalajara, Guadajalara, Mexico

    Salvador Hinojosa

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Hernandez del Rio, A.A., Cuevas, E., Zaldivar, D. (2020). Multi-level Image Thresholding Segmentation Using 2D Histogram Non-local Means and Metaheuristics Algorithms. In: Oliva, D., Hinojosa, S. (eds) Applications of Hybrid Metaheuristic Algorithms for Image Processing. Studies in Computational Intelligence, vol 890. Springer, Cham. https://doi.org/10.1007/978-3-030-40977-7_6

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