Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
권호 표지

Stereo Correspondence Using Graphs Cuts Kernel

그래프 컷 커널을 이용한 스테레오 대응

  • Lee, Yong-Hwan (Dept. of Digital Contents, Wonkwang University) ;
  • Kim, Youngseop (Dept. of Electronic and Electrical Engineering, Dankook University)
  • 이용환 (원광대학교 디지털콘텐츠공학과) ;
  • 김영섭 (단국대학교 전자전기공학과)
  • Received : 2017.06.14
  • Accepted : 2017.06.21
  • Published : 2017.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Given two stereo images of a scene, it is possible to recover a 3D understanding of the scene. This is the primary way that the human visual system estimates depth. This process is useful in applications like robotics, where depth sensors may be expensive but a pair of cameras is relatively cheap. In this work, we combined our interests to implement a graph cut algorithm for stereo correspondence, and performed evaluation against a baseline algorithm using normalized cross correlation across a variety of metrics. Experimental trials revealed that the proposed descriptor exhibited a significant improvement, compared to the other existing methods.

Keywords

References

  1. D. Scharstein and R. Szeliski, "A Taxonomy and Evaluation of Dense Two Frame Stereo Correspondence Algorithms," International Journal of Computer Vision, 2002.
  2. Y. Boykov and V. Kolmogorov, "An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision", IEEE transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 26, no. 9, pp. 1124-1137, 2004. https://doi.org/10.1109/TPAMI.2004.60
  3. V. Kolmogorov and R. Zabih, "Computing visual correspondence with occlusions using graph cuts", International Conference Computer Vision, volume II, 508-515, 2001.
  4. Scharstein, D., Hirschmuller, H., Kitajima, Y., Krathwohl, G., Nesic, N., Wang, X., and Westling, P. "High Resolution Stereo Datasets with Subpixel-Accurate Ground Truth", Lecture Notes in Computer Science Pattern Recognition, 3142.
  5. M. Bleyer and C. Breiteneder, "Stereo Matching - Stateof-the-Art and Research Challenges," Advanced Topics in Computer Vision. Springer, pp. 143-179, 2013.
  6. K.-J. Yoon and I.-S. Kweon, "Locally Adaptive Support-Weight Approach for Visual Correspondence Search", Computer Vision and Pattern Recognition, pp. 924-931, 2005.
  7. M. Bleyer, C. Rhemann, and C. Rother, "Patch match Stereo Matching with Slanted Support Windows," Proceeding on British Machine Vision, 2011.
  8. I. J. Cox, S. L. Hingorani, S. B. Rao, and B. M. Maggs, "A maximum likelihood stereo algorithm", Computer Vision and Image Understanding, 63(3): 542-567, 1996. https://doi.org/10.1006/cviu.1996.0040
  9. Victor Lempitsky, Carsten Rother, and Andrew Blake, "Logcut-efficient graph cut optimization for markov random fields," International Conference on Computer Vision, 2007.
  10. J. Zbontar and Y. LeCun, "Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches", Computer Vision and Pattern Recognition, 1510.05970, 2015.
  11. Mikhail G. Mozerov and Joost van de Weijer, "Accurate Stereo Matching by Two-Step Energy Minimization," IEEE Transactions on Image Processing, vol. 3, issue. 3, 2015.
  12. H. Hirschmuller and D. Scharstein. "Evaluation of cost functions for stereo matching." IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
  13. S. Birchfield and C. Tomasi, "A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 401-406, 1998.