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Image Segmentation Based on Statistical Confidence Intervals

Department of Mathematics, Universitat Politècnica de Catalunya-BarcelonaTech (EEBE), 08034 Barcelona, Spain
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2018, 20(1), 46; https://doi.org/10.3390/e20010046
Received: 29 November 2017 / Revised: 5 January 2018 / Accepted: 10 January 2018 / Published: 11 January 2018
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

Image segmentation is defined as a partition realized to an image into homogeneous regions to modify it into something that is more meaningful and softer to examine. Although several segmentation approaches have been proposed recently, in this paper, we develop a new image segmentation method based on the statistical confidence interval tool along with the well-known Otsu algorithm. According to our numerical experiments, our method has a dissimilar performance in comparison to the standard Otsu algorithm to specially process images with speckle noise perturbation. Actually, the effect of the speckle noise entropy is almost filtered out by our algorithm. Furthermore, our approach is validated by employing some image samples. View Full-Text
Keywords: image segmentation; statistical confidence interval; filtering; Otsu segmentation; speckle noise image segmentation; statistical confidence interval; filtering; Otsu segmentation; speckle noise
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Buenestado, P.; Acho, L. Image Segmentation Based on Statistical Confidence Intervals. Entropy 2018, 20, 46.

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