Abstract
Segmentation is one of the most difficult tasks in digital image processing. This paper presents a novel segmentation algorithm, which uses a biologically inspired paradigm known as artificial ant colonies. Considering the features of artificial ant colonies, we present an extended model applied in image segmentation. Each ant in our model is endowed with the ability of memorizing a reference object, which will be refreshed when a new target is found. A fuzzy connectedness measure is adopted to evaluate the similarity between the target and the reference object. The behavior of one ant is affected by the neighboring ants and the cooperation between ants is performed by exchanging information through pheromone updating. The simulated results show the efficiency of the new algorithm, which is able to preserve the detail of the object and is insensitive to noise.
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