Difference between revisions of "Publications/huynh.19.prl"
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| title = Connected Filters on Generalized Shape-Spaces |
| title = Connected Filters on Generalized Shape-Spaces |
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| journal = Pattern Recognition Letters |
| journal = Pattern Recognition Letters |
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| + | | volume = 128 |
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| ⚫ | | abstract = Classical hierarchical image representations and connected filters work on sets of connected components (CC). |
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| + | | pages = 348 to 354 |
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| ⚫ | | abstract = Classical hierarchical image representations and connected filters work on sets of connected components (CC). These approaches can be defective to describe the relations between disjoint objects or partitions on images. In practice, objects can be made of several connected components in images (due to occlusions for example)therefore it can be interesting to be able to take into account the relationship between these components to be able to detect the whole object. In Mathematical Morphology, second-generation connectivity (SGC) and tree-based shape-space study this relation between the connected components of an image. However, they have limitations. For this reason, we propose in this paper an extension of the usual shape-space paradigm into what we call a Generalized Shape-Space (GSS). This new paradigm allows to analyze any graph of connected components hierarchically and to filter them thanks to connected operators. |
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| lrdepaper = http://www.lrde.epita.fr/dload/papers/huynh.19.prl.pdf |
| lrdepaper = http://www.lrde.epita.fr/dload/papers/huynh.19.prl.pdf |
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| lrdekeywords = Image |
| lrdekeywords = Image |
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| type = article |
| type = article |
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| id = huynh.19.prl |
| id = huynh.19.prl |
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| + | | identifier = doi:10.1016/j.patrec.2019.09.018 |
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| bibtex = |
| bibtex = |
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@Article<nowiki>{</nowiki> huynh.19.prl, |
@Article<nowiki>{</nowiki> huynh.19.prl, |
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title = <nowiki>{</nowiki>Connected Filters on Generalized Shape-Spaces<nowiki>}</nowiki>, |
title = <nowiki>{</nowiki>Connected Filters on Generalized Shape-Spaces<nowiki>}</nowiki>, |
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journal = <nowiki>{</nowiki>Pattern Recognition Letters<nowiki>}</nowiki>, |
journal = <nowiki>{</nowiki>Pattern Recognition Letters<nowiki>}</nowiki>, |
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| − | year = |
+ | year = 2019, |
| + | volume = <nowiki>{</nowiki>128<nowiki>}</nowiki>, |
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| + | pages = <nowiki>{</nowiki>348--354<nowiki>}</nowiki>, |
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| + | month = dec, |
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| + | doi = <nowiki>{</nowiki>10.1016/j.patrec.2019.09.018<nowiki>}</nowiki>, |
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abstract = <nowiki>{</nowiki>Classical hierarchical image representations and connected |
abstract = <nowiki>{</nowiki>Classical hierarchical image representations and connected |
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filters work on sets of connected components (CC). These |
filters work on sets of connected components (CC). These |
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Latest revision as of 12:47, 24 November 2020
- Authors
- Lê Duy Huỳnh, Nicolas Boutry, Thierry Géraud
- Journal
- Pattern Recognition Letters
- Type
- article
- Keywords
- Image
- Date
- 2019-09-20
Abstract
Classical hierarchical image representations and connected filters work on sets of connected components (CC). These approaches can be defective to describe the relations between disjoint objects or partitions on images. In practice, objects can be made of several connected components in images (due to occlusions for example)therefore it can be interesting to be able to take into account the relationship between these components to be able to detect the whole object. In Mathematical Morphology, second-generation connectivity (SGC) and tree-based shape-space study this relation between the connected components of an image. However, they have limitations. For this reason, we propose in this paper an extension of the usual shape-space paradigm into what we call a Generalized Shape-Space (GSS). This new paradigm allows to analyze any graph of connected components hierarchically and to filter them thanks to connected operators.
Documents
Bibtex (lrde.bib)
@Article{ huynh.19.prl,
author = {L\^e Duy {Hu\`ynh} and Nicolas Boutry and Thierry G\'eraud},
title = {Connected Filters on Generalized Shape-Spaces},
journal = {Pattern Recognition Letters},
year = 2019,
volume = {128},
pages = {348--354},
month = dec,
doi = {10.1016/j.patrec.2019.09.018},
abstract = {Classical hierarchical image representations and connected
filters work on sets of connected components (CC). These
approaches can be defective to describe the relations
between disjoint objects or partitions on images. In
practice, objects can be made of several connected
components in images (due to occlusions for example),
therefore it can be interesting to be able to take into
account the relationship between these components to be
able to detect the whole object. In Mathematical
Morphology, second-generation connectivity (SGC) and
tree-based shape-space study this relation between the
connected components of an image. However, they have
limitations. For this reason, we propose in this paper an
extension of the usual shape-space paradigm into what we
call a Generalized Shape-Space (GSS). This new paradigm
allows to analyze any graph of connected components
hierarchically and to filter them thanks to connected
operators.}
}