@article{boulmerka_generalized_2014,

title = {A generalized multiclass histogram thresholding approach based on mixture modelling},

author = {A. Boulmerka and M. Saïd Allili and S. Ait-Aoudia},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888328869&doi=10.1016%2fj.patcog.2013.09.004&partnerID=40&md5=d8b872bd0abe9e6c4d52439f8ec360bc},

doi = {10.1016/j.patcog.2013.09.004},

issn = {00313203},

year  = {2014},

date = {2014-01-01},

journal = {Pattern Recognition},

volume = {47},

number = {3},

pages = {1330–1348},

abstract = {This paper presents a new approach to multi-class thresholding-based segmentation. It considerably improves existing thresholding methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions using mixtures of generalized Gaussian distributions (MoGG). The proposed approach seamlessly: (1) extends the standard Otsu's method to arbitrary numbers of thresholds and (2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. MoGGs enable efficient representation of heavy-tailed data and multi-modal histograms with flat or sharply shaped peaks. Experiments on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. © 2013 Elsevier Ltd. All rights reserved.},

keywords = {Arbitrary number, Conditional distribution, Gaussian distribution, Gaussian noise (electronic), Generalized Gaussian Distributions, Graphic methods, Histogram thresholding, Image segmentation, Minimum error thresholding, Mixture-modelling, Mixtures, State-of-the-art techniques, Statistical methods, Thresholding, Thresholding methods},

pubstate = {published},

tppubtype = {article}

}