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...
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ftminesparistech:oai:HAL:hal-01104256v2 2024-05-19T07:42:42+00:00 Bagging Stochastic Watershed on Natural Color Image Segmentation Franchi, Gianni Angulo, Jesus Centre de Morphologie Mathématique (CMM) Mines Paris - PSL (École nationale supérieure des mines de Paris) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL) Reykjavik, Iceland 2015 https://hal.science/hal-01104256 https://hal.science/hal-01104256/document https://hal.science/hal-01104256/file/Bagging_Stochastic_Watershed_FRANCHI_ANGULO_V2.pdf https://doi.org/10.1007/978-3-319-18720-4_36 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-18720-4_36 hal-01104256 https://hal.science/hal-01104256 https://hal.science/hal-01104256/document https://hal.science/hal-01104256/file/Bagging_Stochastic_Watershed_FRANCHI_ANGULO_V2.pdf doi:10.1007/978-3-319-18720-4_36 info:eu-repo/semantics/OpenAccess International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing https://hal.science/hal-01104256 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⟩ Berkeley segmentation database unsupervised image segmentation stochastic watershed [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] info:eu-repo/semantics/conferenceObject Conference papers 2015 ftminesparistech https://doi.org/10.1007/978-3-319-18720-4_36 2024-04-25T00:50:46Z 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. Conference Object Iceland MINES ParisTech: Open Archive (HAL) 422 433 |
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MINES ParisTech: Open Archive (HAL) |
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language |
English |
topic |
Berkeley segmentation database unsupervised image segmentation stochastic watershed [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
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Berkeley segmentation database unsupervised image segmentation stochastic watershed [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Franchi, Gianni Angulo, Jesus Bagging Stochastic Watershed on Natural Color Image Segmentation |
topic_facet |
Berkeley segmentation database unsupervised image segmentation stochastic watershed [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
description |
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. |
author2 |
Centre de Morphologie Mathématique (CMM) Mines Paris - PSL (École nationale supérieure des mines de Paris) Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL) |
format |
Conference Object |
author |
Franchi, Gianni Angulo, Jesus |
author_facet |
Franchi, Gianni Angulo, Jesus |
author_sort |
Franchi, Gianni |
title |
Bagging Stochastic Watershed on Natural Color Image Segmentation |
title_short |
Bagging Stochastic Watershed on Natural Color Image Segmentation |
title_full |
Bagging Stochastic Watershed on Natural Color Image Segmentation |
title_fullStr |
Bagging Stochastic Watershed on Natural Color Image Segmentation |
title_full_unstemmed |
Bagging Stochastic Watershed on Natural Color Image Segmentation |
title_sort |
bagging stochastic watershed on natural color image segmentation |
publisher |
HAL CCSD |
publishDate |
2015 |
url |
https://hal.science/hal-01104256 https://hal.science/hal-01104256/document https://hal.science/hal-01104256/file/Bagging_Stochastic_Watershed_FRANCHI_ANGULO_V2.pdf https://doi.org/10.1007/978-3-319-18720-4_36 |
op_coverage |
Reykjavik, Iceland |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing https://hal.science/hal-01104256 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⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-18720-4_36 hal-01104256 https://hal.science/hal-01104256 https://hal.science/hal-01104256/document https://hal.science/hal-01104256/file/Bagging_Stochastic_Watershed_FRANCHI_ANGULO_V2.pdf doi:10.1007/978-3-319-18720-4_36 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1007/978-3-319-18720-4_36 |
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422 |
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433 |
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