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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International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, 2015, Reykjavik, Iceland. 9082, pp.422-433, Lecture Notes in Computer Science. 〈10.1007/978-3-319-18720-4_36〉
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Soumis le : mercredi 18 mars 2015 - 13:53:15
Dernière modification le : vendredi 27 octobre 2017 - 17:36:02

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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. 9082, pp.422-433, Lecture Notes in Computer Science. 〈10.1007/978-3-319-18720-4_36〉. 〈hal-01104256〉

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