分割
模糊逻辑
人工智能
能量(信号处理)
计算机科学
区域增长
图像分割
GSM演进的增强数据速率
模式识别(心理学)
计算机视觉
数学
尺度空间分割
图像(数学)
统计
作者
Jiangxiong Fang,Huaxiang Liu,Liting Zhang,Jun Liu,Hesheng Liu
标识
DOI:10.1016/j.ins.2020.08.078
摘要
This paper raises a region-edge-based active contour driven by the hybrid and local fuzzy region-based energy to segment images with high noise and intensity inhomogeneity. The energy functional consists of region energy and edge energy. The region energy is made up of hybrid fuzzy region term and local fuzzy region term. Its aim is to motivate initial contour to move toward the exact object boundary. What’s more, it is proved to be convex and ensures the segmentation results independent of initialization. The hybrid fuzzy region term can balance the importance of the object and background while the local fuzzy region term by incorporating spatial and local information can decrease the effect of intensity inhomogeneity in given images. The edge energy is used to regularize the pseudo level set function (LSF) and maintain the appearance of the smoothness during the curve evolution. Inspired by the fuzzy energy-based active contour (FEAC), a more direct and simpler method is developed to calculate the difference between the old and new energy functions to update the pseudo LSF during the curve evolution. Experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models. The code is available at: https://github.com/fangchj2002/HLFRA.
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