A generalized multiclass histogram thresholding approach based on mixture modelling

Authors:
Aïssa Boulmerka;Mohand Saïd Allili;Samy Ait-Aoudia
Affiliations:
-;-;-
Venue:
Pattern Recognition
Year:
2014

Citing 19
Cited 0

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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.