View source for Publications/huynh.19.prl

{{Publication
| published = true
| date = 2019-09-20
| authors = Lê Duy Huỳnh, Nicolas Boutry, Thierry Géraud
| title = Connected Filters on Generalized Shape-Spaces
| journal = Pattern Recognition Letters
| volume = 128
| pages = 348 to 354
| 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.
| lrdepaper = http://www.lrde.epita.fr/dload/papers/huynh.19.prl.pdf
| lrdekeywords = Image
| lrdenewsdate = 2019-09-20
| type = article
| id = huynh.19.prl
| identifier = doi:10.1016/j.patrec.2019.09.018
| bibtex = 
@Article<nowiki>{</nowiki>	  huynh.19.prl,
  author	= <nowiki>{</nowiki>L\^e Duy <nowiki>{</nowiki>Hu\`ynh<nowiki>}</nowiki> and Nicolas Boutry and Thierry G\'eraud<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>Connected Filters on Generalized Shape-Spaces<nowiki>}</nowiki>,
  journal	= <nowiki>{</nowiki>Pattern Recognition Letters<nowiki>}</nowiki>,
  year		= 2019,
  volume	= <nowiki>{</nowiki>128<nowiki>}</nowiki>,
  pages		= <nowiki>{</nowiki>348--354<nowiki>}</nowiki>,
  month		= dec,
  doi		= <nowiki>{</nowiki>10.1016/j.patrec.2019.09.018<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>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.<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}