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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8321308 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT vacavantantoine smoothedshockfilteringalgorithmandapplications |