title: Semi-Huber potential function for image segmentation.
abstract: In this work, a novel model of Markov Random Field (MRF) is introduced. Such a model is based on a proposed Semi-Huber potential function and it is applied successfully to image segmentation in presence of noise. The main difference with respect to other half-quadratic models that have been taken as a reference is, that the number of parameters to be tuned in the proposed model is smaller and simpler. The idea is then, to choose adequate parameter values heuristically for a good segmentation of the image. In that sense, some experimental results show that the proposed model allows an easier parameter adjustment with reasonable computation times.
authors.affiliation: Unidad Academica de Ingenieriıa Electrica, Universidad Autonoma de Zacatecas, Av. Lopez Velarde 801, Col. Centro, C. P. 98000, Zacatecas, Zacatecas, Mexico. osvaldo_gtz_mt@hotmail.com
authors.forename: Osvaldo
authors.initials: O
authors.lastname: Gutiérrez
authors.forename: Ismael
authors.initials: I
authors.lastname: de la Rosa
authors.forename: Jesús
authors.initials: J
authors.lastname: Villa
authors.forename: Efrén
authors.initials: E
authors.lastname: González
authors.forename: Nivia
authors.initials: N
authors.lastname: Escalante
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity