DOI QR코드

DOI QR Code

Local Stereo Matching Method based on Improved Matching Cost and Disparity Map Adjustment

개선된 정합 비용 및 시차 지도 재생성 기반 지역적 스테레오 정합 기법

  • Kang, Hyun Ryun (Department of Information and Communication Engineering Sungkyunkwan University) ;
  • Yun, In Yong (Department of Information and Communication Engineering Sungkyunkwan University) ;
  • Kim, Joong Kyu (Department of Information and Communication Engineering Sungkyunkwan University)
  • 강현련 (성균관대학교 정보통신대학) ;
  • 윤인용 (성균관대학교 정보통신대학) ;
  • 김중규 (성균관대학교 정보통신대학)
  • Received : 2016.09.07
  • Accepted : 2017.04.10
  • Published : 2017.05.25

Abstract

In this paper, we propose a stereo matching method to improve the image quality at the hole and the disparity discontinuity regions. The stereo matching method extracts disparity map finding corresponding points between stereo image pair. However conventional stereo matching methods have a problem about the tradeoff between accuracy and precision with respect to the length of the baseline of the stereo image pair. In addition, there are hole and disparity discontinuity regions which are caused by textureless regions and occlusion regions of the stereo image pair. The proposed method extracts initial disparity map improved at disparity discontinuity and miss-matched regions using modified AD-Census-Gradient method and adaptive weighted cost aggregation. And then we conduct the disparity map refinement to improve at miss-matched regions, while also improving the accuracy of the image. Experimental results demonstrate that the proposed method produces high-quality disparity maps by successfully improving miss-matching regions and accuracy while maintaining matching performance compared to existing methods which produce disparity maps with high matching performance. And the matching performance is increased about 3.22(%) compared to latest stereo matching methods in case of test images which have high error ratio.

본 논문에서는 홀 영역과 시차 불연속 영역을 개선하기 위한 스테레오 정합 기법을 제안한다. 스테레오 정합 기법은 두 영상에서의 정합 점을 탐색하여 시차 지도를 추출한다. 하지만 기존의 스테레오 정합 기법들은 스테레오 영상의 베이스 라인 길이에 따라서 정확도와 정밀도가 반비례하는 문제점이 있다. 또한 영상의 폐색 영역과 특징 부족으로 인한 시차 불연속 영역이 존재한다. 제안한 기법에서는 개선된 AD-Census-Gradient 방법과 적응적 가중치 기반의 비용 결합을 통하여 불연속 영역과 오 정합 영역을 개선한 초기 시차 지도를 추출하였다. 그 후에 시차 지도 재생성 과정을 수행하여 오정합 영역을 개선함과 동시에 영상의 정밀도를 개선하였다. 실험 결과 제안하는 기법이 기존의 정합률이 높은 방법들과 비교하여 높은 수준의 정합률을 유지하면서 오정합 영역과 정밀도를 개선하였음을 보였다. 그리고 정합 오차율이 높은 영상의 경우, 최근에 발표된 스테레오 정합 방법들보다 정합 성능이 평균적으로 3.22(%)가량 증가하였다.

Keywords

References

  1. Xing Mei, Xun Sun, Mingcai Zhou, Shaohui Jiao, Haitao Wang and Xiaopeng Zhang, "On Building an Accurate Stereo Matching System on Graphic Hardware," in Proc. of IEEE Conf. on Computer Vision Workshops, pp. 467-474, Barcelona, Spain, Nov 2011.
  2. K. Czarnecki, S. Helsen, "Feature-based survey of model transformation approaches," IBM Systems Journal, Vol. 45, no. 3, pp. 621-645, 2006. https://doi.org/10.1147/sj.453.0621
  3. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, no. 11, pp. 1222-1239, Aug 2002.
  4. J. Sun, N. Zheng, and H. Y. Shum, "Stereo matching using belief propagation," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 25, no. 7, pp. 787-800, June 2003. https://doi.org/10.1109/TPAMI.2003.1206509
  5. Yilei Zhang, Minglun Gong and Yee-Hong Yang, "Local Stereo Matching with 3D Adaptive Cost Aggregation for Slanted Surface Modeling and Sub-pixel Accuracy," in Proc. of IEEE Conf. on Pattern Recognition, pp. 1-4, Tampa, USA, Dec 2008.
  6. D. Scharstein and R. Szeliski, "A taxonomy and evaluation of dense two frame stereo correspondence algorithms," International Journal of Computer Vision, Vol. 47, no. 1, pp. 7-42, Apr 2002. https://doi.org/10.1023/A:1014573219977
  7. Linchao Bao, Yibing Song, Qingxiong Yang, Hao Yuan and Gang Wang, "Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree," IEEE Trans. Image Processing, Vol. 23, no. 2, pp. 555-569, Nov 2013. https://doi.org/10.1109/TIP.2013.2291328
  8. Ziyang Ma, Kaiming He, Yichen Wei, Jian Sun and Enhua Wu, "Constant Time Weighted Median Filtering for Stereo Matching and Beyond," in Proc. of IEEE Conf. on Computer Vision, pp. 49-56, Sydney, Australia, Dec 2013.
  9. F. Tomasi, S. Mattoccia and L. D. Stefano, "Segmentation based adaptive support for accurate stereo correspondence," Pacific-Rim Symposium on Image and Video Technology, Springer, pp. 427-438, Dec 2007.
  10. K. J. Yoon and I. S. Kweon, "Adaptive support-weight approach for correspondence search," IEEE Trans. Pattern Analysis and Machinde Intelligence, Vol. 28, no. 4, pp. 650-656, Feb 2006. https://doi.org/10.1109/TPAMI.2006.70
  11. http://vision.middlebury.edu/stereo/
  12. Qingxiong Yang, "A non-local cost aggregation method for stereo matching," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1402-1409, Rhode Island, USA, June 2012.
  13. Fatih Porikli, "Constant time O(1) bilateral filtering," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-8, Anchorage, USA, June 2008.
  14. Kaiming He, Jian Sun, and Xiaoou Tang, "Guided Image Filtering," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 35, no. 6, pp. 1397-1409, Oct 2012. https://doi.org/10.1109/TPAMI.2012.213
  15. Y. Zhan, Y. Gu, K. Huang, C. Zhang and K. Hu, "Accurate image-guided stereo matching with efficient matching cost and disparity refinement," IEEE Trans. Circuits and Systems for Video Technology, Vol. 26, no. 9, pp. 1632-1645, Aug 2015. https://doi.org/10.1109/TCSVT.2015.2473375
  16. M. Mozerov and J. van Weijer, "Accurate stereo matching by two step Energy optimization," IEEE Trans. Image Processing, Vol. 24, no. 3, pp. 1153-1163, Mar 2015. https://doi.org/10.1109/TIP.2015.2395820
  17. J. Liu, C. Li, F. Mei, and Z. Wang, "3D entity-based stereo matching with ground control points and joint second order smoothness prior," The Visual Computer, Sep 2015.
  18. G. Kordelas, D. Alexiadis, P. Daras, and E. Izquierdo, "Enhanced disparity estimation in stereo images," Image and Vision Computing, Mar 2015.
  19. Kang Zhang, Yuqiang Fang, Dongbo Min, Lifeng Sun, Shiqiang Yang, Shuicheng Yan and Qi Tian, "Cross-Scale Cost Aggregation for Stereo Matching," in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1590-1597, Columbus, USA, June 2014.
  20. Yong Seok Heo, Kyoung Mu Lee, and Sang Uk Lee, "Robust Stereo Matching Using Adaptive Normalized Cross-Correlation," IEEE Trans. Pat-tern Analysis and Machine Intelligence, Vol. 33, no. 4, pp. 807-822, April 2011. https://doi.org/10.1109/TPAMI.2010.136