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." />
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Cellular Watersheds: A Parallel Implementation of the Watershed Transform on the CNN Universal Machine

Seongeun EOM
Vladimir SHIN
Byungha AHN

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E90-D    No.4    pp.791-794
Publication Date: 2007/04/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e90-d.4.791
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
Keyword: 
watershed transform,  parallel implementation,  cellular neural network universal machine,  image segmentation,  

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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.

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