An Efficient Algorithm for Connected Attribute Thinnings and Thickenings

From LRDE

Revision as of 17:57, 4 January 2018 by Bot (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

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.}
}