DOI QR코드

DOI QR Code

3차원 체적 모델의 생성을 위한 색상 최적화 함수 기반의 조명 보상 기법

A New Illumination Compensation Method based on Color Optimization Function for Generating 3D Volumetric Model

  • 투고 : 2020.04.22
  • 심사 : 2020.06.12
  • 발행 : 2020.07.30

초록

본 논문에서는 실사 3차원 모델 생성용 다시점 카메라 시스템을 통해 획득된 영상에 대한 조명 보상 기법을 제안하고자 한다. 3차원 체적에 대한 촬영은 실내에서 이루어지고 시간에 따른 조명의 위치와 강도는 일정하다고 가정한다. 다시점 카메라는 총 8대를 사용하고, 공간의 중심을 향해서 수렴하는 형태이므로 조명이 일정하다고 할지라도 각 카메라에 입사되는 빛의 강도 및 각도는 다르다. 따라서 모든 카메라는 색상 보정 차트를 촬영하고, 색상 최적화 함수를 이용하여 획득된 8개의 영상 사이의 관계를 정의하는 색상 변환 매트릭스를 획득한다. 이것을 이용하여 색상 보정 차트를 기준으로 모든 카메라로부터 입력되는 영상을 보정한다. 본 논문은 3차원 객체를 8대의 카메라를 이용해 영상 취득할 시 카메라 간의 색차를 최소화하기 위한 컬러 보정 방법을 제안한 것으로 3차원 영상으로 복원 시 영상 간의 색차가 줄어드는 것을 실험적으로 증명하였다.

In this paper, we propose a color correction technique for images acquired through a multi-view camera system for acquiring a 3D model. It is assumed that the 3D volume is captured indoors, and the position and intensity of the light is constant over time. 8 multi-view cameras are used, and converging toward the center of the space, so even if the lighting is constant, the intensity and angle of light entering each camera may be different. Therefore, a color optimization function is applied to a color correction chart taken from all cameras, and a color conversion matrix defining a relationship between the obtained 8 images is calculated. Using this, the images of all cameras are corrected based on the standard color correction chart. This paper proposed a color correction method to minimize the color difference between cameras when acquiring an image using 8 cameras of 3D objects, and experimentally proved that the color difference between images is reduced when it is restored to a 3D image.

키워드

참고문헌

  1. R. Schafer, P. Kauff, R. Skupin, Y. Sanchez and C. WeiBig, "Interactive Steaming of Panoramas and VR Worlds," SMPTE Motion Imaging Journal, Vol.126, No.1, pp. 35-42, Jan.-Feb. 2017. https://doi.org/10.5594/JMI.2016.2640058
  2. T. Nguyen, T. Qui, K. Xu, A. Cheok, S. Teo, Z. Zhou, A. Mallawaarachchi, S. Lee, W. Liu, H. Teo, L. Thang, Y. Li, H. Kato, "Real-time 3D human capture system for mixed-reality art and entertainment," IEEE Transactions on Visualization and Computer Graphics, Vol.11, No.6, pp. 706-721, November 2005. https://doi.org/10.1109/TVCG.2005.105
  3. Z. Zhan, G. Zhou and X. Yang, "A Method of Hierarchical Image Retrieval for Real-Time Photogrammetry Based on Multiple Features," IEEE Access, Vol.8, pp. 21524-21533, January 2020. https://doi.org/10.1109/ACCESS.2020.2969287
  4. Photogrammetry, https://en.wikipedia.org/wiki/Photogrammetry (accessed May. 10, 2020).
  5. S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodeges, D. Freeman and A. Davison, A. Fitzgibbon, "KinectFusion: Real-Time Dynamic 3D Surface Reconstruction and Interaction Using a Moving Depth Camera," International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2011, Vancouver, Canada, pp. 127-136, 2011.
  6. S. Choi, S. Park, "Convenient View Calibration of Multiple RGB-D Cameras Using a Spherical Object," KIPS Transactions on Software and Data Engineering, Vol.3, No.8, pp.309-314, August 2014. https://doi.org/10.3745/KTSDE.2014.3.8.309
  7. K. Kim, B. Park, D. Kim and Y. Seo,"Point Cloud Registration Algorithm Based on RGB-D Camera for Shooting Volumetric Objects," Journal of Broadcast Engineering, Vol.24, No.5, September 2019.
  8. A. Rizzi, C. Gatta and D. Marini, "A new algorithm for unsupervised global and local color correction," Pattern Recognition Letters, Vol.24, No.11, pp.1663-1677, July 2003. https://doi.org/10.1016/S0167-8655(02)00323-9
  9. E. Provenzi. C. Gatta, M. Fierro and A. Rizzi, "A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.30, No.10, pp.1757-1770, August 2008. https://doi.org/10.1109/TPAMI.2007.70827
  10. V. Vonikakis, I. Andreadis and A. Gasteratos, "Fast centre-surround contrast modication," IET Image processing, Vol.2, No.1, pp.19-34, February 2008. https://doi.org/10.1049/iet-ipr:20070012
  11. H. Le, H. Li, "Fused logarithmic transform for contrast enhancement," Electronics Letters, Vol, 44. No.1, pp.19-20, January 2008. https://doi.org/10.1049/el:20082182
  12. C. Schlick, Quantization techniques for visualization of high dynamic range pictures, Photorealistic rendering techniques, Berlin and Heidelberg, pp.7-20. 1994.
  13. W. Cao, R. Che and D. Ye, "An illumination-independent edge detection and fuzzy enhancement algorithm based on wavelet transform for non-uniform weak illumination images," Pattern Recognition Letters, Vol.29, No.3, pp.192-199, February 2008. https://doi.org/10.1016/j.patrec.2007.09.012
  14. C. Kuo, N. Yang, C. Liu, P. Tseng and C. Chang, "An effective and exible image enhancement algorithm in compressed domain," Multimedia Tools and Applications, Vol.75, No.2, pp.1177-1200. November 2016. https://doi.org/10.1007/s11042-014-2363-x
  15. T. Kong, N. Isa."Enhancer-based contrast enhancement technique for non-uniform illumination and low-contrast images," Multimedia Tools and Applications, pp.14305-14326. August 2016.
  16. Y. Lai, P. Tsai, C. Yao and S. Ruan, "Improved local histogram equalization with gradient-based weighting process for edge preservation," Multimedia Tools and Applications, Vol.76, pp.1585-1613, December 2015.
  17. S. Dubey, S. Singh and R. Singh, "A multi-channel based illumination compensation mechanism for brightness invariant image retrieval," Multimedia Tools and Applications, Vol.74, No.24, pp.11223-11253. August 2014. https://doi.org/10.1007/s11042-014-2226-5
  18. Y. Rao, L. Hou, Z. Wang and L. Chen, "Illumination-based nighttime video contrast enhancement using genetic algorithm," Multimedia Tools and Applications, Vol.70, No.3, pp.2235-2254, September 2012. https://doi.org/10.1007/s11042-012-1226-6
  19. J. Shen, X. Yang, Y. Jia and X. Li, "Intrinsic images using optimization," Computer Vision and Pattern Recognition, Providence, USA, pp.3481-3487, 2011.
  20. Y. Han, Z. Zhang, "An ecient estimation method for intensity factor of illumination changes," Multimedia Tools and Applications, Vol.72, No.3, pp.2619-2632, July 2013. https://doi.org/10.1007/s11042-013-1521-x
  21. A. Kushwaha, R. Srivastava, "Automatic moving object segmentation methods under varying illumination conditions for video data comparative study, and an improved method," Multimedia Tools and Applications, Vol.75 No.23, pp.16209-16264, September 2015.
  22. B. Curless, M. Levoy. "volumetric method for building complex models from range images," ACM Transactions on Graphics (SIGGRAPH), New York, USA, pp.303-312, 1996.