Thresholding-based segmentation revisited using mixtures of generalized Gaussian distributions
A Boulmerka, MS Allili - … of the 21st International Conference on …, 2012 - ieeexplore.ieee.org
This paper presents a new approach to image-thresholding-based segmentation. It
considerably improves existing methods by efficiently modeling non-Gaussian and multi-
modal class-conditional distributions. The proposed approach seamlessly: 1) extends the
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. We
use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling …
considerably improves existing methods by efficiently modeling non-Gaussian and multi-
modal class-conditional distributions. The proposed approach seamlessly: 1) extends the
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. We
use the recently-proposed mixture of generalized Gaussian distributions (MoGG) modeling …
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