2016ImageSegmentationUsingAO

Abstract: In image analysis, segmentation is considered one of the most important steps. Segmentation by searching threshold values assumes that objects in a digital image can be modeled through distinct gray level distributions. In this chapter it is proposed the use of a bio-inspired algorithm, called Allostatic Optimisation (AO), to solve the multi threshold segmentation problem. Our approach considers that an histogram can be approximated by a mixture of Cauchy functions, whose parameters are evolved by AO. The contributions of this chapter are on three fronts, by using: a Cauchy mixture to model the original histogram of digital images, the Hellinger distance as an objective function, and AO algorithm. In order to illustrate the proficiency and robustness of the proposed approach, it has been compared to the well-known Otsu method, over several standard benchmark images.