Bagging Stochastic Watershed on Natural Color Image Segmentation
International audience 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 int...
Main Authors: | , |
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Other Authors: | , , |
Format: | Conference Object |
Language: | English |
Published: |
HAL CCSD
2015
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Subjects: | |
Online Access: | https://hal.archives-ouvertes.fr/hal-01104256 https://hal.archives-ouvertes.fr/hal-01104256/document https://hal.archives-ouvertes.fr/hal-01104256/file/Bagging_Stochastic_Watershed_FRANCHI_ANGULO_V2.pdf https://doi.org/10.1007/978-3-319-18720-4_36 |
Summary: | International audience 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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