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A generalized multiclass histogram thresholding approach based on mixture modelling
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DOI：10.1016/j.patcog.2013.09.004.png)
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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. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Image segmentation
Multi-modal class thresholding
Mixture of generalized Gaussian distributions (MoGG)
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IF:
7.6
论文数:
1.3W
被引数:
4.5W
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Image thresholding based on the EM algorithm and the generalized Gaussian distribution.png)
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