A Unified Tensor Level Set for Image Segmentation

Abstract--This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt- and pepper-type noise. Second, considering the local geometrical features, e.g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a wei...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Bin Wang, Xinbo Gao, Dacheng Tao, Xuelong Li
• Keywords: Artificial Intelligence, Local Geometrical Features, Model, Tensor, TSMC 2010