Abstract
Ant colony optimization (ACO) is an optimization algorithm inspired by the natural collective behavior of ant species. The ACO technique is exploited in this paper to develop a novel image edge detection approach. The proposed approach is able to establish a pheromone matrix that represents the edge presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of ants are driven by the local variation of the image’s intensity values. Extensive experimental results are provided to demonstrate the superior performance of the proposed approach.
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Tian, J., Yu, W., Chen, L., Ma, L. (2011). Image Edge Detection Using Variation-Adaptive Ant Colony Optimization. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence V. Lecture Notes in Computer Science, vol 6910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24016-4_2
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DOI: https://doi.org/10.1007/978-3-642-24016-4_2
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