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Proceedings Paper

An improved random walk algorithm based on data-adaptive gaussian smoother for image segmentation
Author(s): Cuimei Guo; Sheng Zheng; Yaocheng Xie; Wei Hao
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Paper Abstract

To improve the performance of traditional random walk algorithm, an image segmentation algorithm is proposed, which combined random walk and data-adaptive gaussian smoother. Because the medical or remote sensing images are often occupied by strong noises, a data-adaptive anisotropic filtering technique is proposed to remove noise, The filtering technique built on top of an iterative scheme that can preserve the original significant structures while suppressing the noises to the largest extent, and then compute the gradient image of the filtering image. At last the weights of edges of random walk are determined by both the gray value of original image and the salient features of data-adaptive gaussian smoother. The experimental results from synthetic as well as real images demonstrate that the proposed approach is more effective, accurate and more robust in the noise.

Paper Details

Date Published: 8 December 2011
PDF: 6 pages
Proc. SPIE 8003, MIPPR 2011: Automatic Target Recognition and Image Analysis, 800313 (8 December 2011); doi: 10.1117/12.902072
Show Author Affiliations
Cuimei Guo, China Three Gorges Univ. (China)
Sheng Zheng, China Three Gorges Univ. (China)
Yaocheng Xie, China Three Gorges Univ. (China)
Wei Hao, China Three Gorges Univ. (China)

Published in SPIE Proceedings Vol. 8003:
MIPPR 2011: Automatic Target Recognition and Image Analysis
Tianxu Zhang; Nong Sang, Editor(s)

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