基于水平集的自适应保边平滑分割

为了提高活动轮廓模型对自然图像的分割效果，提出了一种新的分割算法。首先将水平集和全变分有机结合，建立了保边平滑分割模型；其次运用聚类算法自适应选取平衡参数，避免了水平集曲线收敛于局部最优；最后根据水平集对不同平滑分量分割区域不同，设计了基于区域置信度的分割平滑收敛函数，解决了分割曲线消失问题。实验表明，该算法对自然图像分割测评分数高于传统活动轮廓分割算法，对图像纹理和噪声不敏感。

Adaptive Edge-Preserved Smoothing Segmentation on Level Set

To improve the performance of the active contour segmentation algorithm on natural images, a novel segmentation algorithm is proposed. First, combining the level set with the total variation, an edge-preserving smoothing segmentation model is constructed. Then a kind of clustering algorithm is employed to learn the balance parameter adaptively to avoid the level set curve converges at the local optimal point. At last, according to the different smoothing components with different segmentation regions, the segmentation smoothing convergence function based on regional confidence is designed to solve segmentation curve vanishes. Experimental results show that the score of this algorithm is higher than that of the traditional active-contour-based segmentation algorithmsfor the real images, and the algorithm is insensitive to texture and noise.