Level Set Contour Extraction Based On Data-Adaptive Gaussian Smoother

This paper presents a new object contour extraction method, which combines the level set evolution with the data-adaptive Gaussian smoother. It analyzes image under the framework of local data-adaptived Gaussian smoother and uses the local adaptive Gaussian kernels to represent salient features underlying image. The Gaussian filter, used in conventional level set method to compute the edge indicator, is replaced by the data-adaptive Gaussian smoother. The level set evolution method is implemented on the feature image obtained by convolving the data-adaptive Gaussian smoother with the original image. The proposed level set contour extraction method based on adaptive Gaussian smoother (LSAG), has been tested on both synthetic and real images. Comparisons with other methods, such as level set evolution without re-initialization (LSWR), demonstrate that the proposed LSAG method has advantages in extracting contours of the noise and weak contrast image and level set evolution speed.