26 October 2013 Small target detection using min-cut for non-balanced graph
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Proceedings Volume 8918, MIPPR 2013: Automatic Target Recognition and Navigation; 89181C (2013) https://doi.org/10.1117/12.2031403
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Detection of infrared dim small target is an important task in many application fields such as automatic target detection, target search and tracking, and early warning. By combining the block-based background reconstruction and min-cut of non-balanced graph, a dim small target detection algorithm is presented. First, a background reconstruction based on a new modeling is presented. Secondly, the background is suppressed though subtracting the reconstructed image from the original image. Lastly, further segmentation using min-cut for non-balanced graph to the background suppressed image is proposed in order to obtain the binary image containing target. The optimal segmentation threshold is selected by heuristic search based on the optimal min-cut. Experimental results show that the proposed method can suppress background noise and clutter effectively and detect infrared small target accurately.
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Airong Sun, Yihua Tan, Jinwen Tian, "Small target detection using min-cut for non-balanced graph", Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 89181C (26 October 2013); doi: 10.1117/12.2031403; https://doi.org/10.1117/12.2031403
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