TY - JOUR
T1 - Image segmentation using an evolutionary method based on allostatic mechanisms
AU - Osuna-Enciso, Valentín
AU - Zúñiga, Virgilio
AU - Oliva, Diego
AU - Cuevas, Erik
AU - Sossa, Humberto
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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 amixture 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.
AB - 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 amixture 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.
UR - http://www.scopus.com/inward/record.url?scp=84959387485&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28854-3_10
DO - 10.1007/978-3-319-28854-3_10
M3 - Artículo
AN - SCOPUS:84959387485
SN - 1860-949X
VL - 630
SP - 255
EP - 279
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
ER -