关键词: Chan-Vese模型, 视觉认知, 水平集方法, 图像分割

Abstract: Chan-Vese model (CV model) is a deformable curve moving from initial contour to object boundary under the influence of internal image force and external constraint force, which is widely used in a number of application domains including image segmentation, boundary detection and other research areas. However, little has been discussed on initial contour extracting algorithm due to the individual diversity of images. This paper proposes an adaptive CV model segmentation algorithm based on visual perception. Firstly, the priori shape information of target object regions is obtained automatically based on visual attention mechanism and image feature analyzing from bottom to top. Then, a modified CV model with self-adaptive initial contour is presented on this basis for image segmentation. The experimental results demonstrate that the proposed ACV model is robust and adaptive; therefore the impact of initial contours to active contour models is reducing effectively, meanwhile it is easier to implement and faster in computation for image segmentation than traditional CV model.

Key words: Chan-Vese model, visual perception, level set method, image segmentation