Local level set segmentation method combined with narrow band

The paper deduces a general form of energy function from the level set method based on Mumford-Shah model. It introduces the gradient of image features into the energy function, which could make the segmentation more precise and the algorithm converge faster. In order to detect local object in the image with clutter background, a local level set segmentation method using the proposed energy function is presented in this paper. The method combined with narrow band could obtain local optimal segmentation, which just needs prior location of the object. To tackle the problem that the calculation cost of level set is so expensive, the paper proposes an efficient algorithm for narrow band which implements very fast. The algorithm starts with a simple initial curve, and then it only updates the level set function in the narrow band. The local level set method is applied successfully to image segmentation with cluttered background, multi-object detection and moving object detection. The results of the experiments are presented in the end of paper.