期刊
APPLIED SOFT COMPUTING
卷 17, 期 -, 页码 1-11出版社
ELSEVIER
DOI: 10.1016/j.asoc.2013.11.018
关键词
Image segmentation; Optimization; Entropy; Gaussian curve fitting
资金
- [NRF-2013K2A1B9066056]
The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solutionis computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution(DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods. (C) 2013 Elsevier B. V. All rights reserved.
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