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http://dx.doi.org/10.9717/kmms.2013.16.6.756

Small Target Detection Method Using Bilateral Filter Based on Surrounding Statistical Feature  

Bae, Tae-Wuk (경북대학교 전자전기컴퓨터공학부)
Kim, Young-Taeg (국립해양조사원)
Publication Information
Journal of Korea Multimedia Society / v.16, no.6, 2013 , pp. 756-763 More about this Journal
Abstract
Bilateral filter (BF), functioning by two Gaussian filters, domain and range filter is a nonlinear filter for sharpness enhancement and noise removal. In infrared (IR) small target detection field, the BF is designed by background predictor for predicting background not including small target. For this, the standard deviations of the two Gaussian filters need to be changed adaptively in background and target region of an infrared image. In this paper, the proposed bilateral filter make the standard deviations changed adaptively, using variance feature of mean values of surrounding block neighboring local filter window. And, in case the variance of mean values for surrounding blocks is low for any processed pixel, the pixel is classified to flat background and target region for enhancing background prediction. On the other hand, any pixel with high variance for surrounding blocks is classified to edge region. Small target can be detected by subtracting predicted background from original image. In experimental results, we confirmed that the proposed bilateral filter has superior target detection rate, compared with existing methods.
Keywords
Small Target; Bilateral Filter; Infrared Searching; Clutter; False Alarm Rate;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 이승익, 김주영, 김기홍, 구본호, "복잡한 FLIR 영상에서의 소형 표적 탐지 기법," 멀티미디어학회논문지, 제10권, 제4호, pp. 432-440, 2007.   과학기술학회마을
2 A.D. Jong, "IRST and Its Perspective," Proc. of SPIE 2552, pp. 206-213, 1995.
3 W.L. Wolfe, Introduction to Infrared System Design, SPIE Optical Engineering Press, Washington, 1996.
4 L. Chengjun, W. Ying, and S. Zeling, "A Small Target Detection Algorithm Based on Multi-Scale Energy Cross," Proc. 2003 IEEE International Conf. on Robotics, Intelligent System and Signal Processing, Vol. 2, pp. 1191-1196, 2003.
5 S. Deshpande, M. Er, and R. Venkateswarlu, "Detection and Tracking of Moving Point-Targets in IR Images," The Fifth International Conference on Control, Automation, Robotics, and Vision, Vol. FA3, No. 2, pp. 1252-1256, 1998.
6 F. Zhang, C. Li, and L. Shi, "Detecting and Tracking Dim Moving Point Target in IR Image Sequence," Infrared Physics & Technology, Vol. 46, No. 4, pp. 323-328. 2004.
7 Y. Cao, R. Liu, and J. Yang, "Small Target Detection using Two-Dimensional Least Mean Square (TDLMS) Filter Based on Neighbor Analysis," Int. J . Infrared Millim. Waves, Vol. 29, No. 2, pp. 188-200, 2008.   DOI   ScienceOn
8 T.W. Bae, B.I. Kim, Y.C. Kim, and K.I. Sohng, "Small Target Detection using Cross Product Based on Temporal Profile in Infrared Image Sequences," Comput. Electr. Eng., Vol. 36, No. 6, pp. 1156-1164, 2010.   DOI   ScienceOn
9 Y. Xion, J.X. Peng, M. Ding, and D.H. Xue, "An Extended Track-Before-Detect Algorithm for Infrared Target Detection," IEEE Trans. Aerosp. Electron. Syst., Vol. 33, No. 3, pp. 1087-1092, 1997.   DOI   ScienceOn
10 C. Tomasi and R. Manduchi, "Bilateral Filtering for Gray and Color Images," Proc. Int. Conf. Comput. Vis., pp. 839-846, 1998.
11 C.I. Hilliard, "Selection of a Clutter Rejection Algorithm for Real-Time Target Detection from an Airborne Platform," Proc. of SPIE 4048, pp. 74-84, 2000.