区域信息和水平集方法的图像分割

随着图像处理技术不断发展,图像分割技术也在不断的走向成熟,但是目前比较成熟的分割方法都存在一定的局限性,传统的分割方法一般都难以实现全局分割,而且对目标边缘比较模糊的物体难以实现有效的精确的分割;基于区域信息和水平集方法的图像分割算法弥补了这些缺陷,该算法是在传统的动态轮廓GAC模型和C_V模型的基础上进行改善;通过实验分析,首先,该算法极大提高了图像分割的精确性,使得轮廓线能够在要分割目标的边缘附近停止演化,即使目标的边缘是模糊不清的图像,该算法也能实现精确地分割;其次,该算法还克服了传统动态轮廓分割算

Image segmentation based on region and level set method

With the development of image processing technology, the image segmentation technology is also in maturity, however, more segmentation method has some limitations at present, it is very difficult to realize global segmentation with the traditional method, and difficult to realize the efficient and accurate segmentation for the objects with the weak or blurred edge. A novel region-based active contour model is proposed in this paper. It is based on the geodesic active contour GAC and C_V model. Through the experimental analysis, first, the algorithm greatly improves the accuracy of segmentation, the evolution of contour can stop near the edge of object, even if the edge of target is weak or blurred. This algorithm overcomes disadvantages of traditional active contour segmentation algorithm in local segmentation, effectively realized the global segmentation.