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Robust scale invariant target detection using the scale-space theory and optimization for IRST

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Abstract

This paper presents a new small target detection method using scale invariant feature. Detecting small targets whose sizes are varying is very important to automatic target detection in infrared search and track (IRST). The conventional spatial filtering methods with fixed sized kernel show limited target detection performance for incoming targets. The scale invariant target detection can be defined as searching for maxima in the 3D (x, y, and scale) representation of an image with the Laplacian function. The scale invariant feature can detect different sizes of targets robustly. Experimental results with real FLIR images show higher detection rate and lower false alarm rate than conventional methods. Furthermore, the proposed method shows very low false alarms in scan-based IR images than conventional filters.

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Acknowledgments

This research was supported by Yeungnam University research grants in 210-A-054-014.

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Authors and Affiliations

  1. Department of Electronic Engineering, Yeungnam University, 214-1 Dae-dong, Gyeongsan-si, Gyeonsnabuk-do, 712-749, Korea

    Sungho Kim

  2. Electro-Optics Laboratory 3-1-2, Agency for Defense Development, P.O. Box 35-3, Yuseong-gu, Daejeon, 305-600, Korea

    Joo-Hyoung Lee

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  1. Sungho Kim
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  2. Joo-Hyoung Lee
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Correspondence to Sungho Kim.

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Kim, S., Lee, JH. Robust scale invariant target detection using the scale-space theory and optimization for IRST. Pattern Anal Applic 14, 57–66 (2011). https://doi.org/10.1007/s10044-010-0190-x

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  • DOI: https://doi.org/10.1007/s10044-010-0190-x

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