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所属单位：电子信息工程学院

发表刊物：Pattern Recogn.

摘要：This paper proposes a new global and local weighted signed pressure force (SPF) based active contour model (ACM) to segment various types of images. First, by introducing the normalized global minimum absolute differences as the coefficients of global inner and outer region fitting centers, a new global weighted SPF (GWSPF) is defined, which makes the best of the difference information of inner and outer regions and improves segmentation performance. Second, by introducing the normalized local minimum absolute differences as the coefficients of local inner and outer region fitting centers similarly, a new local weighted SPF (LWSPF) is defined and added to the above global weighted SPF. Third, the global and local within-class variances of the image are used to weight the GWSPF and the LWSPF, which can automatically adjust the effect degrees of the GWSPF and the LWSPF. Experiments on many kinds of real-world images have validated that the proposed model is superior to popular ACMs in segmentation accuracy, in addition, it is robust to the initial curve. © 2018 Elsevier Ltd

ISSN号：0031-3203

是否译文：否

发表时间：2019-04-01

合写作者：韩斌

通讯作者：吴一全