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Journal of Electronic Imaging • Open Access

Image segmentation on adaptive edge-preserving smoothing
Author(s): Kun He; Dan Wang; Xiuqing Zheng

Paper Abstract

Nowadays, typical active contour models are widely applied in image segmentation. However, they perform badly on real images with inhomogeneous subregions. In order to overcome the drawback, this paper proposes an edge-preserving smoothing image segmentation algorithm. At first, this paper analyzes the edge-preserving smoothing conditions for image segmentation and constructs an edge-preserving smoothing model inspired by total variation. The proposed model has the ability to smooth inhomogeneous subregions and preserve edges. Then, a kind of clustering algorithm, which reasonably trades off edge-preserving and subregion-smoothing according to the local information, is employed to learn the edge-preserving parameter adaptively. At last, according to the confidence level of segmentation subregions, this paper constructs a smoothing convergence condition to avoid oversmoothing. Experiments indicate that the proposed algorithm has superior performance in precision, recall, and F-measure compared with other segmentation algorithms, and it is insensitive to noise and inhomogeneous-regions.

Paper Details

Date Published: 4 October 2016
PDF: 14 pages
J. Electron. Imag. 25(5) 053022 doi: 10.1117/1.JEI.25.5.053022
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Kun He, Sichuan Univ. (China)
Dan Wang, Sichuan Univ. (China)
Xiuqing Zheng, Sichuan Normal Univ. (China)

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