Level set contour extraction method based on support value filter

Abstract

Active contour method is a powerful approach for object contour extraction. This paper presents a new object contour extraction method, which combines the level set evolution with the support value filter. It analyzes image under the least squares support vector machine (LS-SVM) framework and uses the support values to represent salient features underlying image. The Gaussian filter, used in conventional level set method to compute the edge indicator, is replaced by the support value filter deduced from the mapped LS-SVM. The level set evolution method is implemented on the feature image obtained by convolving the support value filter with the original image. Experiments are undertaken on the synthetic and real images. The experimental results demonstrate that the support value filter can provide a good estimate for the optimal step size in a gradient descent algorithm and the proposed method has advantages over the direct level set evolution method in converging speed and contour extraction accuracy.