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4 March 1996 Extracting peak statistics with recurrent network for detecting dim point-source targets in IR noise
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Abstract
In this paper, peak-statistics-based preprocessing methods for detecting point-source target (PST) in IR images are described, and a neural network connection for extracting peak- statistics with recurrent networks is presented. Because of the strong correlation in IR noise, a sample will have less probability in having a larger intensity than its adjacent samples do, in other words, it will have less probability in becoming a peak. The presence of PST interrupts the consistency of correlate between adjacent samples of IR noise, as a result, turning up the difference in peak-statistics. Based on the features of PST and a detailed analysis on the features of IR noise, we adopt a modified difference operation, namely, bidirectional difference (BD), and a peak-statistics-based threshold operation as the preprocessing step. Theory analyses and simulation results have shown that the performance of the proposed methods is better than normal threshold operation.
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Guan Hua, Lan Tao, Zhenkang Shen, and Zhongkang Sun "Extracting peak statistics with recurrent network for detecting dim point-source targets in IR noise", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); https://doi.org/10.1117/12.234256
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