Abstract: An improved active contour model for edge detection based on Chan and Vese active contour model is proposed.The basic idea is to evolve a curve under constraints from a given image, which is defined as a union of two homogeneous regions representing the object and background respectively.The edge can be detected by seeking a global minimum of an energy function based on the Mumford-Shah functional.The constant term in this model is modified by combining the image gradient information in piecewise constant optimal approximations.This different constant term can be obtained by adjusting the weighting factor that acts on the image gradient term in constant function, and different edge map based on different intensity of image can be obtained.This method is capable of handling changes in the topology of the evolving contour, and can avoid the problem arise in the C-V model that cannot detect the edges whose values are far from the mean intensity value of image.Effectiveness of this method is demonstrated in numerical experiments.

Key words: active contour, level set, Mumford-Shah functional, image processing, edge detection