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A novel thresholding algorithm is presented to achieve improved image segmentation performance at low computational cost in this paper. The proposed algorithm uses a normalized graph cut measure as the thresholding principle to distinguish an object from the background. 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. Therefore, the proposed algorithm occupies much smaller storage space and requires much lower computational costs and implementation complexity than other image segmentation algorithms based on graph cuts. 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.