A New Image Thresholding Method Based on Graph Spectral Theory
Tao Wen · Chinese Journal of Computers · 2007
In this paper,a novel thresholding algorithm is presented to achieve improved image segmentation performance at low computational cost.The proposed algorithm uses the normalized graph cut measure as the thresholding principle to distinguish an object from the background,as such fair treatment of different sets of diversified sizes is ensured.The weight matrices used in evaluating the graph cuts are based on the gray levels of an image,rather than the commonly used image pixels.For most images,the number of gray levels is much smaller than the number of pixels.Therefore,the proposed algorithm occupies much smaller storage space and requires much lower computational costs and implementation complexity than other graph-based image segmentation algorithms.This fact makes the proposed algorithm attractive in various real-time vision applications such as automatic target recognition(ATR).A large number of examples are presented to show the superior performance of the proposed thresholding algorithm compared to existing thresholding algorithms.