Korean Journal of Optics and Photonics (한국광학회지)

Optical Society of Korea (한국광학회)
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Occluded Object Reconstruction and Recognition with Computational Integral Imaging

집적 영상을 이용한 가려진 표적의 복원과 인식

  • Lee, Dong-Su (Department of Computer and Communication Engineering, Daegu University) ;
  • Yeom, Seok-Won (Department of Computer and Communication Engineering, Daegu University) ;
  • Kim, Shin-Hwan (Department of Computer and Communication Engineering, Daegu University) ;
  • Son, Jung-Young (Department of Computer and Communication Engineering, Daegu University)
  • 이동수 (대구대학교 정보통신공학과) ;
  • 염석원 (대구대학교 정보통신공학과) ;
  • 김신환 (대구대학교 정보통신공학과) ;
  • 손정영 (대구대학교 정보통신공학과)
  • Published : 2008.08.25
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Abstract

This paper addresses occluded object reconstruction and recognition with computational integral imaging (II). Integral imaging acquires and reconstructs target information in the three-dimensional (3D) space. The reconstruction is performed by averaging the intensities of the corresponding pixels. The distance to the object is estimated by minimizing the sum of the standard deviation of the pixels. We adopt principal component analysis (PCA) to classify occluded objects in the reconstruction space. The Euclidean distance is employed as a metric for decision making. Experimental and simulation results show that occluded targets are successfully classified by the proposed method.

본 논문에서는 집적 영상의 획득과 복원을 통하여 장애물에 가려진 물체를 인식하는 기술은 제안하고 구현하였다. 집적 영상의 복원은 해당되는 화소 세기의1차 확률적 특성인 평균으로 구한다. 복원평면까지의 거리는 2차 확률적 특성인 표준 편차를 이용하여 구하고3차원 물체의 경계(edge)를 검출한다. 표준 편차의 합을 최소로 하는 거리에서 복원된 영상을 표적인식에 이용한다. 표적인식은 주성분 분석(principle component analysis, PCA) 분류기를 복원된 영상에 적용하였다. 표적 분류에 대한 판정은 분류기에 의해서 투영된 클래스의 평균 특징 벡터와 테스트 특징 벡터간의 유클리드 거리(Euclidean distance)를 이용한다. 실험 및 시뮬레이션을 통하여 가려진 표적을 본 논문에서 제안한 방법을 통하여 오차 없이 분류하였다.

Keywords

References

  1. G. Lippmann, “La photographie integrale,” C. R Acad. Sci. 146, pp. 446-451, 1908
  2. J. -S. Jang and B. Javidi, “Time-multiplexed integral imaging for 3D sensing and display,” Optics and Photonics News, 15, pp. 36-43, 2004
  3. J. -Y. Son, V. V. Saveljev, Y. -J Choi, J. -E. Bahn, S. -K Kim, and H. Choi, “Parameters for designing autostereoscopic imaging systems based on lenticular, parallax barrier, and integral photography plates,” Optics Engineering, 42, pp. 3326-3333, 2003 https://doi.org/10.1117/1.1615259
  4. S. H. Hong, J. S. Jang, and B. Javidi, “Three-dimensional volumetric object reconstruction using computational integral imaging,” Optics Express, 12, pp. 483-491, 2004 https://doi.org/10.1364/OPEX.12.000483
  5. Y. S. Hwang, S. H. Hong, and B. Javidi, “Free view 3-D visualization of occluded objects by using computational synthetic aperture integral imaging,” Journal of Display Technology, 3, pp. 65-70, 2007 https://doi.org/10.1109/JDT.2006.890702
  6. D. -C. Hwang, D. -H. Shin, S. -C. Kim, and E. -S. Kim, “Depth extraction of three-dimensional objects in space by the computational integral imaging reconstruction technique,” Applied Optics, 47, pp. D128-D135, 2008 https://doi.org/10.1364/AO.47.00D128
  7. C. Wu, M. McCormick, A. Aggoun, and S. Y. Kung, “Depth Mapping of Integral Images Through Viewpoint Image Extraction With a Hybrid Disparity Analysis Algorithm,” Journal of Display Technology, 4, pp. 101-108, 2008 https://doi.org/10.1109/JDT.2007.904360
  8. B. Javidi, R. Ponce-Diaz, and S. -H. Hong, “Threedimensional recognition of occluded objects by using computational integral imaging,” Optics Letter, 31, pp. 1106-1108, 2006 https://doi.org/10.1364/OL.31.001106
  9. J. -H. Park, J. Kim, and B. Lee, “Three-dimensional optical correlator using a sub-image array,” Optics Express, 13, pp. 5116-5126, 2005 https://doi.org/10.1364/OPEX.13.005116
  10. J. S. Park, D. C. Hwang, D. H. Shin, E. S. Kim, “Resolutionenhanced 3D image correlator using computationally reconstructed integral images,” Optics Communications, 276, pp.72-79, 2007 https://doi.org/10.1016/j.optcom.2007.04.007
  11. S. Yeom and B. Javidi, “Three-dimensional distortiontolerant object recognition using integral imaging,” Optics Express, 12, pp. 5795-5809, 2004 https://doi.org/10.1364/OPEX.12.005795
  12. 염석원, “광자계수 비선형 정합필터를 이용한 부분적으로 가려진 물체의 패턴인식”, 2007년 광정보처리 기술 및 3차원 디스플레이 워크샵, pp. 35-40, 2007
  13. S. Yeom, B. Javidi, C.-W. Lee, and E. Watson, “Photoncounting passive 3D image sensing for reconstruction and recognition of partially occluded objects,” Optics Express, 15, pp. 16189-16195, 2007 https://doi.org/10.1364/OE.15.016189
  14. 염석원,이동수,김신환,“집적 영상을 이용한 왜곡에 강한 물체인식”, 광전자 및 광통신 학술회의, COOC 2008, 2008
  15. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification 2nd, (Wiley Interscience, New York 2001), Chapt. 3, pp. 114-117

Cited by

  1. Distance Extraction by Means of Photon-Counting Passive Sensing Combined with Integral Imaging vol.15, pp.4, 2011, https://doi.org/10.3807/JOSK.2011.15.4.357
  2. Three-Dimensional Object Reconstruction and Recognition Using Computational Integral Imaging and Statistical Pattern Analysis vol.48, pp.9, 2009, https://doi.org/10.1143/JJAP.48.09LB05