Open AccessSemi-Huber potential function for image segmentation
Author(s) -
O Aquines Gutierrez,
José Ismael De la Rosa Vargas,
Jesús Villa,
Efrén González,
Nivia Escalante
Publication year - 2012
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.20.006542
Subject(s) - markov random field , image segmentation , segmentation , computation , computer science , image (mathematics) , function (biology) , noise (video) , algorithm , artificial intelligence , markov chain , quadratic equation , mathematics , machine learning , geometry , evolutionary biology , biology
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.