Difference between revisions of "Publications/lesage.06.isvc"
From LRDE
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| booktitle = Proceedings of the second International Conference on Visual Computing |
| booktitle = Proceedings of the second International Conference on Visual Computing |
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| address = Lake Tahoe, Nevada, USA |
| address = Lake Tahoe, Nevada, USA |
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− | | lrdeprojects = |
+ | | lrdeprojects = Olena |
| pages = 393 to 404 |
| pages = 393 to 404 |
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| volume = 4292 |
| volume = 4292 |
Revision as of 15:08, 22 February 2017
- Authors
- David Lesage, Jérôme Darbon, Ceyhun Burak Akgül
- Where
- Proceedings of the second International Conference on Visual Computing
- Place
- Lake Tahoe, Nevada, USA
- Type
- inproceedings
- Publisher
- Springer-Verlag
- Projects
- Olena
- Keywords
- Image
- Date
- 2006-08-09
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.} }