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Smoothed Shock Filtering: Algorithm and Applications

This article presents the smoothed shock filter, which iteratively produces local segmentations in image’s inflection zones with smoothed morphological operators (dilations, erosions). Hence, it enhances contours by creating smoothed ruptures, while preserving homogeneous regions. After describing t...

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Detalles Bibliográficos
Autor principal: Vacavant, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321308/
https://www.ncbi.nlm.nih.gov/pubmed/34460712
http://dx.doi.org/10.3390/jimaging7030056
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author Vacavant, Antoine
author_facet Vacavant, Antoine
author_sort Vacavant, Antoine
collection PubMed
description This article presents the smoothed shock filter, which iteratively produces local segmentations in image’s inflection zones with smoothed morphological operators (dilations, erosions). Hence, it enhances contours by creating smoothed ruptures, while preserving homogeneous regions. After describing the algorithm, we show that it is a robust approach for denoising, compared to related works. Then, we expose how we exploited this filter as a pre-processing step in different image analysis tasks (medical image segmentation, fMRI, and texture classification). By means of its ability to enhance important patterns in images, the smoothed shock filter has a real positive impact upon such applications, for which we would like to explore it more in the future.
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spelling pubmed-83213082021-08-26 Smoothed Shock Filtering: Algorithm and Applications Vacavant, Antoine J Imaging Article This article presents the smoothed shock filter, which iteratively produces local segmentations in image’s inflection zones with smoothed morphological operators (dilations, erosions). Hence, it enhances contours by creating smoothed ruptures, while preserving homogeneous regions. After describing the algorithm, we show that it is a robust approach for denoising, compared to related works. Then, we expose how we exploited this filter as a pre-processing step in different image analysis tasks (medical image segmentation, fMRI, and texture classification). By means of its ability to enhance important patterns in images, the smoothed shock filter has a real positive impact upon such applications, for which we would like to explore it more in the future. MDPI 2021-03-15 /pmc/articles/PMC8321308/ /pubmed/34460712 http://dx.doi.org/10.3390/jimaging7030056 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Vacavant, Antoine
Smoothed Shock Filtering: Algorithm and Applications
title Smoothed Shock Filtering: Algorithm and Applications
title_full Smoothed Shock Filtering: Algorithm and Applications
title_fullStr Smoothed Shock Filtering: Algorithm and Applications
title_full_unstemmed Smoothed Shock Filtering: Algorithm and Applications
title_short Smoothed Shock Filtering: Algorithm and Applications
title_sort smoothed shock filtering: algorithm and applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321308/
https://www.ncbi.nlm.nih.gov/pubmed/34460712
http://dx.doi.org/10.3390/jimaging7030056
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