Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Stereo Matching Algorithm Based on Fast Guided Image Filtering for 3-Dimensional Video Service

3차원 비디오 서비스를 위한 고속 유도 영상 필터링 기반 스테레오 매칭 알고리즘

  • Hong, Gwang-Soo (Big data Utilization Research Center, Sookmyung Women's University) ;
  • Kim, Byung-Gyu (Dept. of IT engineering, Sookmyung Women's University)
  • Received : 2016.12.08
  • Accepted : 2016.12.30
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Stereo matching algorithm is an essential part in computer vision and photography. Accuracy and computational complexity are challenges of stereo matching algorithm. Much research has been devoted to stereo matching based on cost volume filtering of matching costs. Local stereo matching based guided image filtering (GIF) has a computational complexity of O(N), but is still not enough to provide real-time 3-dimensional (3-D) video services. The proposed algorithm concentrates reduction of computational complexity using the concept of fast guided image filter, which increase the speed up to $O(N/\small{s}^2)$ with a sub-sampling ratio $\small{s}$. Experimental results indicated that the proposed algorithm achieves effective local stereo matching as well as a fast execution time for 3-D video service.

스테레오 매칭 알고리즘은 컴퓨터 비전과 사진촬영에 필수적인 알고리즘으로 정확도와 복잡도는 스테레오 매칭 알고리즘의 주요 문제점이었다. 그 중에서도 복잡도가 높은 문제점을 극복하기 위해 비용 볼륨 필터링을 기반한 스테레오 매칭 알고리즘에 대한 많은 연구가 이루어졌다. 지역 스테레오 매칭 기술인 유도 영상 필터링 기술은 O(N)의 복잡도를 가지고 있지만, 여전히 실시간 3D 비디오 서비스를 제공하기에는 계산량이 많은 편이다. 따라서 본 논문에서 고속 유도 영상 필터링에 기반한 스테레오 매칭 알고리즘을 제안한다. 고속 유도 필터링은 서브샘플 비율 $\small{s}$에 따라 복잡도 $O(N/\small{s}^2)$을 가지는 알고리즘이다. 제안하는 알고리즘은 효과적인 스테레오 알고리즘을 성능을 보여줌과 동시에 3D 서비스를 위한 빠른 실행 시간을 보여준다.

Keywords

References

  1. D. Scharstein and R. Szeliski, "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms," International Journal of Computer Vision, vol. 37, no. 1-3, pp. 7-42, April, 2002. https://doi.org/10.1023/A:1008151528479
  2. F. etTombari, S. Mattoccia and L. Di Stefano, "Segmentation-based adaptive support for accurate stereo correspondence," Proceedings of Advances in Image and Video Technology, pp. 427-438, 2007.
  3. N. Y. Kwak, "An object-based stereo matching method using block-based segmentation," Journal of Digital Contents Society, vol. 5, no. 4, pp. 257-263, 2004.
  4. A. Hosni, M. Bleyer, M. Gelautz and C. Rhemann, "Local stereo matching using geodesic support weights," Proceedings of IEEE International Conference on Image Processing, pp. 2093-2096, 2009.
  5. Q. Yang, "A non-local cost aggregation method for stereo matching," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1402-1409, 2008.
  6. K. J. Yoon and I. s. Kwoen, "Adaptive support-weight approach for correspondence search," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 650-656, April, 2006. https://doi.org/10.1109/TPAMI.2006.70
  7. M. Gerrits and P. Bekaert, "Local stereo matching with segmentation-based outlier rejection," The 3rd Canadian Conference on Computer and Robot Vision, pp. 66-72, 2006.
  8. A. Hosni, M. Bleyer, and Margrit Gelautz, "Near realtime stereo with adaptive support weight approaches," International Symposium on 3D Data Processing, Visualization and Transmission, pp. 1-8, 2010.
  9. C. Rhemann, A. Hosni, M. Bleyer, C. Rother and M. Gelautz, "Fast cost-volume filtering for visual correspondence and beyond," IEEE Conference on Computer Vision and Pattern Recognition, pp. 3017-3024, June, 2011.
  10. K. He, J. Sun and X. Tang, "Guided image filtering," 11th European Conference on Computer Vision, pp. 1-14, 2010.
  11. K. he and J. sun, "fast guided filter," arXiv: 1505.00996v1 [cs.CV] 5 May 2015.
  12. F.C. Crow, "Summed-area tables for texture mapping," ACM SIGGRAPH Computer Graphics, pp. 207-212, 1984.
  13. Z. Ma, K. He, Y. Wei, J. Sun and E. Wu, "Constant time weighted median fitering for stereo matching and beyond," IEEE International Conference on Computer Vision, pp. 49-56, December, 2013.
  14. Middlebury Stereo Vision Page, http://vision.middlebury.edu/stereo/

Cited by

  1. Adaptive Inter-layer Filter Selection Mechanism for Improved Scalable Extensions of High Efficiency Video Coding (SHVC) vol.18, pp.1, 2017, https://doi.org/10.9728/dcs.2017.18.1.141