Browse > Article
http://dx.doi.org/10.5573/ieie.2017.54.5.65

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

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)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.5, 2017 , pp. 65-73 More about this Journal
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.
Keywords
stereo matching; disparity discontinuity region; Hole filling; Tree filter; Weighted median filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.
2 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.
3 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.
4 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.
5 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.   DOI
6 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.   DOI
7 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.   DOI
8 G. Kordelas, D. Alexiadis, P. Daras, and E. Izquierdo, "Enhanced disparity estimation in stereo images," Image and Vision Computing, Mar 2015.
9 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.
10 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.   DOI
11 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.
12 K. Czarnecki, S. Helsen, "Feature-based survey of model transformation approaches," IBM Systems Journal, Vol. 45, no. 3, pp. 621-645, 2006.   DOI
13 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.
14 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.   DOI
15 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.
16 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.   DOI
17 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.   DOI
18 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.
19 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.   DOI
20 http://vision.middlebury.edu/stereo/