IEICE TRANSACTIONS on Information
Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine
Summary :
The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.4 pp.791-794
- Publication Date
- 2007/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.4.791
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Keyword
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Seongeun EOM, Vladimir SHIN, Byungha AHN, "Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 4, pp. 791-794, April 2007, doi: 10.1093/ietisy/e90-d.4.791.
Abstract: The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.4.791/_p
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@ARTICLE{e90-d_4_791,
author={Seongeun EOM, Vladimir SHIN, Byungha AHN, },
journal={IEICE TRANSACTIONS on Information},
title={Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine},
year={2007},
volume={E90-D},
number={4},
pages={791-794},
abstract={The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.},
keywords={},
doi={10.1093/ietisy/e90-d.4.791},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine
T2 - IEICE TRANSACTIONS on Information
SP - 791
EP - 794
AU - Seongeun EOM
AU - Vladimir SHIN
AU - Byungha AHN
PY - 2007
DO - 10.1093/ietisy/e90-d.4.791
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2007
AB - The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.
ER -
English
