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Image segmentation using Scale-Space Random Walks

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3 Author(s)
Richard Rzeszutek ; Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria St., Toronto, Ontario, M5B 2K3, Canada ; Thomas El-Maraghi ; Dimitrios Androutsos

Many methods for supervised image segmentation exist. One such algorithm, random walks, is very fast and accurate when compared to other methods. A drawback to random walks is that it has difficulty producing accurate and clean segmentations in the presence of noise. Therefore, we propose an extension to random walks that improves its performance without significantly modifying the original algorithm. Our extension, known as ldquoscale-space random walksrdquo, or SSRW, addresses these problems. The SSRW is able to produce more accurate segmentations in the presence of noise while still retaining all of the properties of the original algorithm.

Published in:

2009 16th International Conference on Digital Signal Processing

Date of Conference:

5-7 July 2009