Difference between revisions of "Publications/geraud.01.icip"
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| address = Thessaloniki, Greece |
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| abstract = We present an original method to segment color images using a classification in the 3-D color space. In the case of ordinary images, clusters that appear in 3-D histograms usually do not fit a well-known statistical model. For that reason, we propose a classifier that relies on mathematical morphology, and more precisely on the watershed algorithm. We show on various images that the expected color clusters are correctly identified by our method. Last, to segment color images into coherent regions, we perform a Markovian labeling that takes advantage of the morphological classification results. |
| abstract = We present an original method to segment color images using a classification in the 3-D color space. In the case of ordinary images, clusters that appear in 3-D histograms usually do not fit a well-known statistical model. For that reason, we propose a classifier that relies on mathematical morphology, and more precisely on the watershed algorithm. We show on various images that the expected color clusters are correctly identified by our method. Last, to segment color images into coherent regions, we perform a Markovian labeling that takes advantage of the morphological classification results. |
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| lrdeslides = http://www.lrde.epita.fr/dload/papers/geraud.01.icip_slides.pdf |
| lrdeslides = http://www.lrde.epita.fr/dload/papers/geraud.01.icip_slides.pdf |
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Latest revision as of 18:57, 4 January 2018
- Authors
- Thierry Géraud, Pierre-Yves Strub, Jérôme Darbon
- Where
- Proceedings of the IEEE International Conference on Image Processing (ICIP)
- Place
- Thessaloniki, Greece
- Type
- inproceedings
- Projects
- Olena
- Keywords
- Image
- Date
- 2001-10-01
Abstract
We present an original method to segment color images using a classification in the 3-D color space. In the case of ordinary images, clusters that appear in 3-D histograms usually do not fit a well-known statistical model. For that reason, we propose a classifier that relies on mathematical morphology, and more precisely on the watershed algorithm. We show on various images that the expected color clusters are correctly identified by our method. Last, to segment color images into coherent regions, we perform a Markovian labeling that takes advantage of the morphological classification results.
Documents
Bibtex (lrde.bib)
@InProceedings{ geraud.01.icip,
author = {Thierry G\'eraud and Pierre-Yves Strub and J\'er\^ome
Darbon},
title = {Color image segmentation based on automatic morphological
clustering},
booktitle = {Proceedings of the IEEE International Conference on Image
Processing (ICIP)},
year = 2001,
volume = 3,
pages = {70--73},
address = {Thessaloniki, Greece},
month = oct,
abstract = {We present an original method to segment color images
using a classification in the 3-D color space. In the case
of ordinary images, clusters that appear in 3-D histograms
usually do not fit a well-known statistical model. For that
reason, we propose a classifier that relies on mathematical
morphology, and more precisely on the watershed algorithm.
We show on various images that the expected color clusters
are correctly identified by our method. Last, to segment
color images into coherent regions, we perform a Markovian
labeling that takes advantage of the morphological
classification results.}
}