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On minimum variance thresholding
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DOI：10.1016/j.patrec.2006.04.012.png)
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Abstract
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Variance-based thresholding methods could be biased from the threshold found by expert and the underlying mechanism responsible for this bias is explored in this paper. An analysis on the minimum class variance thresholding (MCVT) and the Otsu method, which minimizes the within-class variance, is carried out. It turns out that the bias for the Otsu method is due to differences in class variances or class probabilities and the resulting threshold is biased towards the component with larger class variance or larger class probability. The MCVT method is found to be similar to the minimum error thresholding. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
image thresholding
centroid
class variance
class probability
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Fuzzy partition of two-dimensional histogram and its application to thresholding.png)
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