Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST |
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Authors: | Sungho Kim Yukyung Yang Joohyoung Lee Yongchan Park |
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Affiliation: | (1) Agency for Defense Development, P.O. Box 35-3, 3-1-2, Yuseong-gu, Daejeon, 305-600, Korea |
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Abstract: | Robust detection of small targets is very important in IRST (Infrared Search and Track). This paper presents a novel mathematical
method for the incoming target detection problem in cluttered background motivated from the robust properties of human visual
system (HVS). The HVS shows the best efficiency and robustness for an object detection task. The robust properties of the
HVS are contrast mechanism, multi-resolution representation, size adaptation, and pop-out phenomena. Based on these facts,
a plausible computational model integrating these facts is proposed using Laplacian scale-space theory and Tune-Max based
optimization method. Simultaneous target signal enhancement and background clutter suppression is achieved by tuning and maximizing
the signal-to-clutter ratio (TMSCR) in Laplacian scale-space. At the first stage, the Tune-Max of the signal to background
contrast produces candidate targets with adapted scale. At the second stage, the Tune-Max of the signal-to-clutter ratio (SCR)
produces maximal SCR which is used to pop-out detections. Experimental evaluation results for the incoming target sequence
validate the upgraded detection capability of the proposed method compared with the Top-hat method at the same false alarm
rate. Experimental results for the six kinds of cluttered background images show that the proposed TMSCR produces less false
alarms (4.3 times reduction) compared to the Top-hat at the same detection rate. |
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Keywords: | Incoming small target Background clutter Human visual system Signal-to-clutter ratio Scale-space Tune-Max Scale invariant IRST |
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