대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

유사변환에 불변인 국부적 특징과 광역적 특징 선택에 의한 자동 표적인식

Automatic Target Recognition by selecting similarity-transform-invariant local and global features

  • 선선귀 (韓國科學技術院 電子電算學科 電氣 및 電子工學) ;
  • 박현욱 (韓國科學技術院 電子電算學科 電氣 및 電子工學)
  • Sun, Sun-Gu (Department o Electrical Engineering and Computer Science, Division of Elcetrical Engineering, Korea Advanced Institute of Science and Technology) ;
  • Park, Hyun-Wook (Department o Electrical Engineering and Computer Science, Division of Elcetrical Engineering, Korea Advanced Institute of Science and Technology)
  • 발행 : 2002.07.01
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초록

전방 관측 적외선 영상에서 가려짐이 없거나 가려짐이 있는 군용차량을 인식할 수 있는 자동 표적인식 알고리즘을 제안한다. 표적을 배경으로부터 분리한 후에 광역적인 형상 특징을 찾기 위해 표적의 경계선에 대해 물체의 중심을 기준으로 방사함수 (radial function)를 정의한다. 또한, 형상 정보가 집중되어 있는 표적의 윗 부분으로부터 국부적인 형상 특징을 찾기 위해 두 개의 특징점과 경계선으로부터 거리함수를 정의한다. 두 개의 함수와 경계선으로부터 4개의 광역적 형상 특징과 4개의 국부적 형상 특징을 제안한다. 이 특징들은 병진, 회전 그리고 크기변화에 대해 기존의 특징 벡터들 보다 좋은 불변성을 가진다. 이 특징들을 이용하여 가려짐이 있는 표적과 가려짐이 없는 표적을 구분하여 인식하기 위한 새로운 분류 방식을 제안한다. 실험을 통해 제안한 특징들의 불변성과 인식 성능을 기존의 특징벡터들과 비교하여 제안한 표적 인식 알고리즘의 우수성을 입증한다.

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

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참고문헌

  1. B. Bhanu, Automatic target recognition: state of the art survey, IEEE Trans. Aerosp. Electron.Syst. Vol. 22 (4), pp. 364-379, 1986 https://doi.org/10.1109/TAES.1986.310772
  2. L. Wang, S. Z. Der, N. M. Nasrabadi, 'Automatic target recogniion using a feature-decompostion modular neural network, IEEE Trand. Image Processing, Vol. 7 (8), pp. 1113-1121, 1998 https://doi.org/10.1109/83.704305
  3. J. M. DeCatrel, J. R. Surdu, Practical recognition of armored vehicles in FLIR, Proc. SPIE, Automatic Object Recognition V, Vol. 2485, pp. 200-208,1995
  4. D. Nair, J. K. Aggarwal, Bayesian recognition of targets by parts in second generation forward looking images, Image and Vis. Computing, Vol, 18, pp. 849-864, 2000 https://doi.org/10.1016/S0262-8856(99)00084-0
  5. S. G. Sun, H. W. Park, Segmentation of forward-looking infrared image using fuzzy thresholding and edge detection, Opt. Eng., Vol. 40 (11), pp. 2683-2645, 2001. https://doi.org/10.1117/1.1409563
  6. S. G. Sun, H. W. park, Automatic target recognition using target boundary information in FLIR images, Proc. The LASTED Imt. Conf. Singnal and Image Processing, pp. 405-410, 2000
  7. S. G. Sun, H. W. Park, Invariant feature extraction based on radial and distance function for automatic target recognition, Proc. IEEE Int. Conf. Image Processing, will be presented, 2002 https://doi.org/10.1109/ICIP.2002.1038976
  8. B. Ernisse, S. K. Rogers, M. P. Desimio, R. A. Raines, Complete automatic target cuer recognition system for tactical forward-looking in frared images, Opt. Eng. Vol. 36 (9), pp 2593-2603, 1997 https://doi.org/10.1117/1.601484
  9. A. Panapakkam, S. N. Balakrishnan, Automatic target recognition system using wavelet transform and cluster analysis, Proc. SPIE, Signal Processing, Sensor Fusion, and Target Recognition IV, Vol. 2484, pp. 636-643, 1995 https://doi.org/10.1117/12.213057
  10. S. K. Rogers et. al, Neural networks for automatic target recognition, Neural networks, Vol. 8 (7/8), pp. 1153-1184, 1990 https://doi.org/10.1016/0893-6080(95)00050-X
  11. D. Casasent, A. Ye, Detection filters and algorithm fusion for ATR'. IEEE Trans. Image Processing, Vol. 6 (1), pp. 114-125,1997 https://doi.org/10.1109/83.552101
  12. S. A. Dudani, K. J. Breeding, Aircraft identification by moment invariants', IEEE Trans. Computer, Vol. C-26 (1), pp. 39-45, 1977 https://doi.org/10.1109/TC.1977.5009272
  13. A. P. Reeves, R. J. Prokop, S. E. Anderews, F. P Kuhl, Three-dimentional shape analysis using moments and Fourier descriptors, IEEE Trans. Patt. Anal. Mach. INT. Vol. 10 (6), pp. 937-943, 1988 https://doi.org/10.1109/34.9115
  14. M. R. Teague, Image analysis via the genernal theory of moments, J. Opt. Am. Vol. 70 (8), pp. 920-930, 1980 https://doi.org/10.1364/JOSA.70.000920
  15. N. Ezer, E. Anarim, B. Sankur, A comparative study of moment invariants and Fourier descriptors in planar shape recognition, Proc Seventh Mediterranean Electro-technical Conf. IEEE, pp.242 245, 1994 https://doi.org/10.1109/MELCON.1994.380923
  16. A. K. Jain, Fundementals of digital image processing, Prentice-Hill, Englewood Cliffs, 1989, pp. 342-430
  17. A. Jain, D. Zongker, Feature selection evaluation, application, and small sample perfomance, IEEE Trans. Patt. Analysis and Machine Intelligence, Vol. 19, No. 2, pp. 153-158, 1997 https://doi.org/10.1109/34.574797