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Bagging Stochastic Watershed on Natural Color Image Segmentation

Abstract : The stochastic watershed is a probabilistic segmentation ap-proach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bag-ging. Re-sampling and bagging is a classical stochastic approach to im-prove the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.
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Submitted on : Wednesday, March 18, 2015 - 1:53:15 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:13 PM


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Gianni Franchi, Jesus Angulo. Bagging Stochastic Watershed on Natural Color Image Segmentation. International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, 2015, Reykjavik, Iceland. pp.422-433, ⟨10.1007/978-3-319-18720-4_36⟩. ⟨hal-01104256⟩



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