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Integrating Metaheuristic Algorithms and Minimum Cross Entropy for Image Segmentation in Mist Conditions

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Metaheuristics in Machine Learning: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 967))

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Abstract

Presently, there are several problems with the artificial vision of the autonomous cars, one of these is the detection of objects in adverse conditions, a typical example of this problem is rain where visibility is limited or non-existent, in the same way, autonomous driving under sand or snow storms or in mist conditions, just to mention a few. The core of this chapter deals with the problem in mist conditions by improving image segmentation. These types of images represent a challenge that requires the application of efficient and robust computer techniques that enhance; the segmentation, contrast and can be clearly interpreted. This work proposes the use of metaheuristic algorithms (MA) combined with threshold technique such as minimum cross entropy (MCE) to applied to images under mist conditions. The results of the work are analyzed under statistical tests and to guarantee proper segmentation several state-of-the-art metrics were used.

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Author information

Authors and Affiliations

  1. División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico

    Mario A. Navarro & Diego Oliva

  2. Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico

    Daniel Zaldívar

  3. Facultad Informática, Dpto. Ingeniería de Software e Inteligencia Artificial, Universidad Complutense de Madrid, Madrid, Spain

    Gonzalo Pajares

Authors
  1. Mario A. Navarro
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  2. Diego Oliva
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  3. Daniel Zaldívar
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  4. Gonzalo Pajares
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Corresponding author

Correspondence to Mario A. Navarro .

Editor information

Editors and Affiliations

  1. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Diego Oliva

  2. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

  3. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Salvador Hinojosa

Appendix

Appendix

The parameters used in each method have been configured according to the reported values in which their best performance is achieved, below in Table 8 is the configuration of these settings, every algorithm was tested using 50 particles of population.

Table 8 Parameter settings
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Navarro, M.A., Oliva, D., Zaldívar, D., Pajares, G. (2021). Integrating Metaheuristic Algorithms and Minimum Cross Entropy for Image Segmentation in Mist Conditions. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_22

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