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Median-based image thresholding
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DOI：10.1016/j.imavis.2011.06.003.png)
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Abstract
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In order to select an optimal threshold for image thresholding that is relatively robust to the presence of skew and heavy-tailed class-conditional distributions, we propose two median-based approaches: one is an extension of Otsu's method and the other is an extension of Kittler and Illingworth's minimum error thresholding. We provide theoretical interpretation of the new approaches, based on mixtures of Laplace distributions. The two extensions preserve the methodological simplicity and computational efficiency of their original methods, and in general can achieve more robust performance when the data for either class is skew and heavy-tailed. We also discuss some limitations of the new approaches. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Image segmentation
Image thresholding
Laplace distributions
Mean absolute deviation from the median (MAD)
Minimum error thresholding (MET)
Otsu's method
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