基于区域显著性的活动轮廓分割模型

提出一种新的活动轮廓分割模型，结合视觉显著性检测机制自动获取待分割图像中目标物体的先验形状信息，并自适应地构造初始轮廓，从而降低了初始轮廓位置对分割算法的影响.同时实现了活动轮廓模型对图像的自适应分割和自动分割，使得分割结果更符合人类视觉感知特性.实验结果表明，该模型有较好的分割效果，迭代次数少，且运行时间短.

An Active Contour Model Based on Region Saliency for Image Segmentation

Image segmentation refers to the process of partitioning an image into some no-overlapped meaningful regions, and it is vital for the higher-level image processing such as image analysis and understanding. During the past few decades, there has been substantial progress in the field of image segmentation and its application. Recently, segmentation algorithms based on active contours have been given wide attention by many internal and foreign researchers due to their variable forms, flexible structure and excellent performance. However, most available active contour models suffer from lacking adaptive initial contour and priori information of target region. In this paper, an active contour model for image segmentation based on visual saliency detection mechanism is proposed. Firstly, priori shape information of target objects in input images which is used to describe the initial curve adaptively is extracted with the visual saliency detection method in order to reduce the influence of initial contour position. Furthermore, the proposed active model can segment images adaptively and automatically, and the segmented results accord with the property of human visual perception. Experimental results demonstrate that the proposed model can achieve better segmentation results than some traditional active contour models. Meanwhile it requires less iteration and is much more computationally efficient.