基于覆盖分割和活动轮廓模型的图像边缘提取算法

针对传统算法对边界模糊的图像分割效果不理想，分割结果多毛刺的问题，提出了一种由粗到细的图像边缘提取方法，主要由像素覆盖分割方法和Chan-Vese模型组成。将改进的覆盖分割方法和活动轮廓模型相结合，首先使用原始覆盖分割算法对图像进行分割，利用多方向模糊形态学边缘检测算法提取不同物体之间的边界；然后采用改进的像素覆盖分割方法给边界像素重新分配覆盖值；最后，运用活动轮廓算法进行细化的图像边界提取；分别进行了分割结果的定性比较，抗噪性测试以及提取的边缘对比实验。实验结果表明，该方法对具有模糊边界的图像，提取边缘结果优于其他可比文献中提出的方法。

Image Boundary Extraction Based on Pixel Coverage Segmentation and Chan-Vese Model

Aiming at the problem of unsatisfactory image segmentation effect for images with blurred boundary by using traditional algorithms, a coarse-to-fine approach for image boundary extraction is proposed in this paper, which is made up of pixel coverage segmentation and Chan-Vese model. Based on modified coverage segmentation algorithm and active-contours method, images are firstly segmented by using the original coverage segmentation algorithm and a multi-directions fuzzy morphological boundary detection algorithm is used to extract the boundaries between different objects. Then an improved pixel coverage segmentation method is applied to redistribute coverage values for boundary pixels. Finally, the boundary extraction for refined images is carried out with active-contours algorithm. And qualitative comparison of segmentation results, noise immunity tests and contrast experiments on the extracted boundary are carried out. Experimental results show that the proposed method can obtain more excellent boundary extraction effect than those state-of-the-art methods proposed in comparable literatures.