Legendre Based Adaptive Image Segmentation Combining The Gradient Information

In this paper, we propose an adaptive variable exponent level set method based on Legendre polynomials for object segmentation in complex visual environment. First, we use a set of Legendre basis functions to approximate the region intensity, which enable us to accommodate heterogeneous objects. Second, an improved function is presented to update exponent adaptively and ensures the image gradient information embedding into the model easily. The proposed method is robust to low contrast, blurred boundaries, noise and the 10-cation of initial contour, and sufficient in handling large scale intensity variations. Experimental results demonstrate that the proposed method can achieve relatively high segmentation accuracy and less computational time.