한국항행학회논문지 (Journal of Advanced Navigation Technology)

  • 제23권5호
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  • Pages.400-407
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  • 2019
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  • 1226-9026(pISSN)
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  • 2288-842X(eISSN)
한국항행학회 (The Korean Navigation Institute)
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항공 영상 분석을 위한 고유영상과 멀티 스케일 감마 보정 기반의 그림자 복원

Shadow Reconstruction Based on Intrinsic Image and Multi-Scale Gamma Correction for Aerial Image Analysis

  • 박기홍 (목원대학교 융합컴퓨터미디어학부)
  • Park, Ki-hong (Division of Convergence Computer & Media, Mokwon University)
  • 투고 : 2019.08.13
  • 심사 : 2019.10.28
  • 발행 : 2019.10.31
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초록

본 논문에서는 다양한 조도의 영향에도 본질적인 특성이 변하지 않는 고유영상을 이용한 그림자 검출과 멀티 스케일 감마 보정 기반의 그림자 복원 방법을 제안하였다. 그림자 검출은 컬러 영상의 그레이스케일 영상과 고유영상 간의 화소 변화 정보를 이용하여 추정하였으며, 그림자 복원 과정에서는 감마 보정을 통해 영상의 밝기를 조절하는 방법을 적용하였다. 감마 보정은 개별적 화소값에 대한 비선형 조정으로 채도가 변경될 수 있으므로 컬러 영상의 채널별로 수행되는 멀티 스케일 감마 보정을 수행한다. 멀티 스케일 감마 값은 컬러 영상에서 그림자와 그림자가 아닌 영역의 교차 윤곽을 획득한 후 이 정보를 기반으로 추정되며, 결과적으로 서로 다른 유형의 영역 특징을 멀티 스케일 감마 값으로 보정하여 그림자를 복원하였다. 실험 결과, 제안하는 방법이 그림자가 포함된 단일 자연 영상에서 그림자를 효과적으로 복원함을 보였다.

In this paper, the shadow detection and reconstruction method are proposed using intrinsic image, which does not change the essential characteristics under the influence of various illuminance, and multi-scale gamma correction. The shadow detection was estimated by the pixel change information between a grayscale and an intrinsic image of the color image, and the brightness of the image were adjusted by gamma correction in the shadow restoration process. Multi-scale gamma correction is performed for each channel of a color image due to the fact that the saturation can be changed by nonlinear adjustment to individual pixel values. Multi-scale gamma values are estimated based on the information of the crossed edge between shadows and non-shadowed regions in the color image, as a result, the shadows are reconstructed by correcting different region features with multi-scale gamma values. Experimental results show that the proposed method effectively reconstructs shadows in a single natural image.

키워드

참고문헌

  1. K. H. Park, “Shadow detection based intensity and cross entropy for effective analysis of satellite image,” Journal of Korea Navigation Technology, Vol. 20, No. 4, pp. 380-385, Aug. 2016. https://doi.org/10.12673/jant.2016.20.4.380
  2. K. H. Park and Y. S. Lee, “Definition and analysis of shadow features for shadow detection in single natural image,” Journal of Digital Contents Society, Vol. 19, No. 1, pp. 165-171, Jan. 2018. https://doi.org/10.9728/dcs.2018.19.1.165
  3. Y. Xiao, E. Tsougenis and C.-K. Tang, "Shadow removal from single RGB-D images," in Proceedings of 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, Issue 2, Columbus: OH, pp. 3011-3018, Jun. 2014.
  4. R. K. Nale and S. A. Shinde, "A novel processing chain for shadow detection and reconstruction in VHR images," International Journal of Advances in Engineering & Technology, Vol. 6, Issue 6, pp. 2724-2731, Jan. 2014.
  5. G. D. Finlayson, M. S. Drew and C. Lu, "Entropy minimization for shadow removal," International Journal of Computer Vision, Vol. 85, Issue 1, pp. 35-57, Oct. 2009. https://doi.org/10.1007/s11263-009-0243-z
  6. S. H. Khan, M. Bennamoun, F. Sohel and R. Togneri, “Automatic shadow detection and removal from a single image,” Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 38, No. 3, pp. 431-446, Mar. 2016. https://doi.org/10.1109/TPAMI.2015.2462355
  7. W. Maddern, A. Stewart, C. McManus, B. Upcroft, W. Churchill, and P. Newman, "Illumination invariant imaging: applications in robust vision-based localisation, mapping and classification for autonomous vehicles," in Proceedings of the Visual Place Recognition in Changing Environments Workshop, IEEE Int. Conf. on Robotics and Automation (ICRA), Hong Kong, Vol. 2, p. 3, May 2014.
  8. M. S. Drew, G. D. Finlayson and S. D. Hordley, "Recovery of chromaticity image free from shadows via illumination invariance," in Proceedings of IEEE Workshop on Color and Photometric Methods in Computer Vision (ICCV), Nice: France, pp. 32-39, Oct. 2003.
  9. G. Finlayson, S. Hordley, C. Lu, and M. Drew, “On the removal of shadows from images,” Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 28, No. 1, pp. 59-68, Jan. 2006. https://doi.org/10.1109/TPAMI.2006.18
  10. Wikipedia. Planckian locus [Internet]. Available: https://en.wikipedia.org/wiki/Planckian_locus.
  11. Wikipedia. Lambertian reflectance [Internet]. Available: https://en.wikipedia.org/wiki/Lambertian_reflectance.
  12. D. W. Scott, Multivariate Density Estimation: Theory, Practice and Visualization, 2nd ed. New Jersey, NJ: John Wiley and Sons, Inc., 2015.
  13. R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital Image Processing using MATLAB, 1st ed. New Jersey, NJ: Pearson Prentice Hall, ch. 2, pp. 66-68, 2004.
  14. A. Criminisi, P. Perez and K. Toyama, "Object removal by exemplar-based inpainting," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Madison: WI, pp. 739-743, June, 2003.
  15. M. S. V. Jyothirmai, K. Srinivas and V. Srinivasa Rao, “Enhancing shadow area using RGB color space,” IOSR Journal of Computer Engineering (IOSRJCE), Vol. 2, No. 1, pp. 24-28, Aug. 2012. https://doi.org/10.9790/0661-0212428