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所属单位：电子信息工程学院

发表刊物：PATTERN RECOGNITION

关键字：Image segmentation Remote sensing river image Active contour model Modified symmetric cross entropy Chebyshev distance

摘要：The traditional active contour models cannot segment the remote sensing river images accurately. To solve this problem, a novel active contour model based on modified symmetric cross entropy is proposed. In the proposed model, the external energy constraint terms are defined by the symmetric cross entropy and the region fitting centers are represented by the medians of pixel grayscale values inside the object and background regions. Moreover, the penalty energy term is incorporated into the energy functional to avoid the re-initialization. In order to improve the segmentation efficiency of the proposed model, the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center is chosen as its region energy weight, which can be adaptively adjusted, instead of the constant region energy weight. The extensive experiments are performed on a large number of remote sensing river images and the results demonstrate that, compared with the CV model, the GAC model, the CEACM model, the RSF model, the LIF model, and the LGIF model, the proposed model can segment the images more accurately and rapidly, which has the clear advantages in both segmentation performance and segmentation efficiency. (C) 2017 Elsevier Ltd. All rights reserved.

ISSN号：0031-3203

是否译文：否

发表时间：2017-07-01

合写作者：韩斌

通讯作者：吴一全