Difference between revisions of "Publications/lesage.06.isvc"

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{{Publication
 
{{Publication
| date = 2006-11-01
+
| published = true
  +
| date = 2006-08-09
 
| authors = David Lesage, Jérôme Darbon, Ceyhun Burak Akgül
 
| authors = David Lesage, Jérôme Darbon, Ceyhun Burak Akgül
 
| title = An Efficient Algorithm for Connected Attribute Thinnings and Thickenings
 
| title = An Efficient Algorithm for Connected Attribute Thinnings and Thickenings
 
| booktitle = Proceedings of the second International Conference on Visual Computing
 
| booktitle = Proceedings of the second International Conference on Visual Computing
 
| address = Lake Tahoe, Nevada, USA
 
| address = Lake Tahoe, Nevada, USA
| project = Image
+
| lrdeprojects = Olena
 
| pages = 393 to 404
 
| pages = 393 to 404
 
| volume = 4292
 
| volume = 4292
 
| series = Lecture Notes in Computer Science Series
 
| series = Lecture Notes in Computer Science Series
 
| publisher = Springer-Verlag
 
| publisher = Springer-Verlag
| urllrde = 200611-ISVC
 
 
| abstract = Connected attribute filters are anti-extensive morphological operators widely used for their ability of simplifying the image without moving its contours. In this paper, we present a fast, versatile and easy-to-implement algorithm for grayscale connected attribute thinnings and thickennings, a subclass of connected filters for the wide range of non-increasing attributes. We show that our algorithm consumes less memory and is computationally more efficient than other available methods on natural images.
 
| abstract = Connected attribute filters are anti-extensive morphological operators widely used for their ability of simplifying the image without moving its contours. In this paper, we present a fast, versatile and easy-to-implement algorithm for grayscale connected attribute thinnings and thickennings, a subclass of connected filters for the wide range of non-increasing attributes. We show that our algorithm consumes less memory and is computationally more efficient than other available methods on natural images.
  +
| lrdekeywords = Image
  +
| lrdenewsdate = 2006-08-09
 
| type = inproceedings
 
| type = inproceedings
 
| id = lesage.06.isvc
 
| id = lesage.06.isvc
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address = <nowiki>{</nowiki>Lake Tahoe, Nevada, USA<nowiki>}</nowiki>,
 
address = <nowiki>{</nowiki>Lake Tahoe, Nevada, USA<nowiki>}</nowiki>,
 
month = nov,
 
month = nov,
project = <nowiki>{</nowiki>Image<nowiki>}</nowiki>,
 
 
pages = <nowiki>{</nowiki>393--404<nowiki>}</nowiki>,
 
pages = <nowiki>{</nowiki>393--404<nowiki>}</nowiki>,
 
volume = 4292,
 
volume = 4292,

Latest revision as of 18:57, 4 January 2018

Abstract

Connected attribute filters are anti-extensive morphological operators widely used for their ability of simplifying the image without moving its contours. In this paper, we present a fast, versatile and easy-to-implement algorithm for grayscale connected attribute thinnings and thickennings, a subclass of connected filters for the wide range of non-increasing attributes. We show that our algorithm consumes less memory and is computationally more efficient than other available methods on natural images.


Bibtex (lrde.bib)

@InProceedings{	  lesage.06.isvc,
  author	= {David Lesage and J\'er\^ome Darbon and Ceyhun Burak Akg\"ul},
  title		= {An Efficient Algorithm for Connected Attribute Thinnings
		  and Thickenings},
  booktitle	= {Proceedings of the second International Conference on
		  Visual Computing},
  year		= 2006,
  address	= {Lake Tahoe, Nevada, USA},
  month		= nov,
  pages		= {393--404},
  volume	= 4292,
  series	= {Lecture Notes in Computer Science Series},
  publisher	= {Springer-Verlag},
  abstract	= {Connected attribute filters are anti-extensive
		  morphological operators widely used for their ability of
		  simplifying the image without moving its contours. In this
		  paper, we present a fast, versatile and easy-to-implement
		  algorithm for grayscale connected attribute thinnings and
		  thickennings, a subclass of connected filters for the wide
		  range of non-increasing attributes. We show that our
		  algorithm consumes less memory and is computationally more
		  efficient than other available methods on natural images.}
}