4 November 2014 Real-time automatic small infrared target detection using local spectral filtering in the frequency
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Abstract
Accurate and fast detection of small infrared target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. Based on human visual attention mechanism, an automatic detection algorithm for small infrared target is presented. In this paper, instead of searching for infrared targets, we model regular patches that do not attract much attention by our visual system. This is inspired by the property that the regular patches in spatial domain turn out to correspond to the spikes in the amplitude spectrum. Unlike recent approaches using global spectral filtering, we define the concept of local maxima suppression using local spectral filtering to smooth the spikes in the amplitude spectrum, thereby producing the pop-out of the infrared targets. In the proposed method, we firstly compute the amplitude spectrum of an input infrared image. Second, we find the local maxima of the amplitude spectrum using cubic facet model. Third, we suppress the local maxima using the convolution of the local spectrum with a low-pass Gaussian kernel of an appropriate scale. At last, the detection result in spatial domain is obtained by reconstructing the 2D signal using the original phase and the log amplitude spectrum by suppressing local maxima. The experiments are performed for some real-life IR images, and the results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be further used for real-time detection and tracking.
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Hao Chen, Hao Chen, Hong Zhang, Hong Zhang, Jiafeng Li, Jiafeng Li, Ding Yuan, Ding Yuan, Mingui Sun, Mingui Sun, } "Real-time automatic small infrared target detection using local spectral filtering in the frequency", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 92730E (4 November 2014); doi: 10.1117/12.2073015; https://doi.org/10.1117/12.2073015
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