View source for Publications/xu.15.pami

{{Publication
| published = true
| date = 2015-06-05
| authors = Yongchao Xu, Thierry Géraud, Laurent Najman
| title = Connected Filtering on Tree-Based Shape-Spaces
| journal = IEEE Transactions on Pattern Analysis and Machine Intelligence
| volume = 38
| number = 6
| pages = 1126 to 1140
| lrdeprojects = Olena
| abstract = Connected filters are well-known for their good contour preservation property. A popular implementation strategy relies on tree-based image representations: for exampleone can compute an attribute characterizing the connected component represented by each node of the tree and keep only the nodes for which the attribute is sufficiently high. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequentlythe filtering is performed not in the space of the imagebut in the space of shapes built from the image. Such a processing of shape-space filtering is a generalization of the existing tree-based connected operators. Indeed, the framework includes the classical existing connected operators by attributes. It also allows us to propose a class of novel connected operators from the leveling family, based on non-increasing attributes. Finally, we also propose a new class of connected operators that we call morphological em shapings. Some illustrations and quantitative evaluations demonstrate the usefulness and robustness of the proposed shape-space filters.
| lrdepaper = http://www.lrde.epita.fr/dload/papers/xu.15.pami.pdf
| lrdekeywords = Image
| lrdenewsdate = 2015-06-05
| type = article
| id = xu.15.pami
| identifier = doi:10.1109/TPAMI.2015.2441070
| bibtex = 
@Article<nowiki>{</nowiki>	  xu.15.pami,
  author	= <nowiki>{</nowiki>Yongchao Xu and Thierry G\'eraud and Laurent Najman<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>Connected Filtering on Tree-Based Shape-Spaces<nowiki>}</nowiki>,
  journal	= <nowiki>{</nowiki>IEEE Transactions on Pattern Analysis and Machine
		  Intelligence<nowiki>}</nowiki>,
  year		= <nowiki>{</nowiki>2016<nowiki>}</nowiki>,
  volume	= <nowiki>{</nowiki>38<nowiki>}</nowiki>,
  number	= <nowiki>{</nowiki>6<nowiki>}</nowiki>,
  pages		= <nowiki>{</nowiki>1126--1140<nowiki>}</nowiki>,
  month		= jun,
  doi		= <nowiki>{</nowiki>10.1109/TPAMI.2015.2441070<nowiki>}</nowiki>,
  abstract	= <nowiki>{</nowiki>Connected filters are well-known for their good contour
		  preservation property. A popular implementation strategy
		  relies on tree-based image representations: for example,
		  one can compute an attribute characterizing the connected
		  component represented by each node of the tree and keep
		  only the nodes for which the attribute is sufficiently
		  high. This operation can be seen as a thresholding of the
		  tree, seen as a graph whose nodes are weighted by the
		  attribute. Rather than being satisfied with a mere
		  thresholding, we propose to expand on this idea, and to
		  apply connected filters on this latest graph. Consequently,
		  the filtering is performed not in the space of the image,
		  but in the space of shapes built from the image. Such a
		  processing of shape-space filtering is a generalization of
		  the existing tree-based connected operators. Indeed, the
		  framework includes the classical existing connected
		  operators by attributes. It also allows us to propose a
		  class of novel connected operators from the leveling
		  family, based on non-increasing attributes. Finally, we
		  also propose a new class of connected operators that we
		  call morphological <nowiki>{</nowiki>\em shapings<nowiki>}</nowiki>. Some illustrations and
		  quantitative evaluations demonstrate the usefulness and
		  robustness of the proposed shape-space filters.<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}