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Multi-ellipses detection on images inspired by collective animal behavior

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Abstract

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact with each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions), while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed, and robustness.

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Authors and Affiliations

  1. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jalisco, Mexico

    Erik Cuevas, Mauricio González, Daniel Zaldívar & Marco Pérez-Cisneros

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  1. Erik Cuevas
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  2. Mauricio González
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  3. Daniel Zaldívar
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  4. Marco Pérez-Cisneros
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Correspondence to Erik Cuevas.

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Cuevas, E., González, M., Zaldívar, D. et al. Multi-ellipses detection on images inspired by collective animal behavior. Neural Comput & Applic 24, 1019–1033 (2014). https://doi.org/10.1007/s00521-012-1332-4

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  • DOI: https://doi.org/10.1007/s00521-012-1332-4

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