Image Segmentation Based on Local Chan Vese Model by Employing Cosine Fitting Energy.

Image segmentation plays a critical role in computer vision and image processing. In this paper, we propose a new Local Chan–Vese (LCV) model by using the cosine function to express the data fitting term in traditional level set image segment models and present a new distance regularized based on a polynomial. We discuss two algorithms of the new model. The first algorithm is a traditional algorithm based on finite difference, which is slow. The second algorithm is a sweeping algorithm, which didn’t need to solve the Euler-Lagrange equation. The second algorithm only needs to calculate the energy change when a pixel was moved from the outside region to the inside region of evolving curves and vice versa. The second algorithm is high speed and can avoid solving the partial differential equation. There is no need for the reinitialization step, and stability conditions, and the distance regularization term. The experiments have shown the effectiveness of the two algorithms.