Advertisement

Computing

pp 1–15 | Cite as

A novel non-parametric transform stereo matching method based on mutual relationship

  • Xiaobo LaiEmail author
  • Xiaomei Xu
  • Lili Lv
  • Zihe Huang
  • Jinyan Zhang
  • Peng Huang
Article
  • 24 Downloads

Abstract

To cope with the problem of the vast majority local stereo matching approaches that rely highly on the statistical characteristics of the image intensity, a novel non-parametric transform stereo matching method based on mutual relationship is proposed. The traditional non-parametric transform is investigated, and its limitations are analyzed. In order to take the pixels’ special location information into consideration during finding stereo correspondences, the original gray values of the neighborhood pixels whose relative position is one unit greater than that of the center pixel are replaced by the gray values interpolation of the four pixels surrounding it. Then the new non-parametric transform stereo matching is performed. The proposed approach is tested with both the standard image datasets and the images captured from realistic scenery. Experimental results are compared to those of intensity-based algorithms; the percentage of bad matching pixels is almost equivalent to the other examined algorithms, and the proposed algorithm exhibits robust behavior in realistic conditions.

Keywords

Stereo matching Non-parametric transform Mutual relationship Bilinear interpolation Disparity map 

Mathematics Subject Classification

05C70 Factorization, matching, covering and packing 

Notes

Acknowledgements

This work is funded in part by National Natural Science Foundation of China (Grant No. 61602419), and also supported by Natural Science Foundation of Zhejiang Province of China (Grant Nos. LY16F10008, LQ16F020003).

Compliance with ethical standards

Conflict of interest

We declare that all authors have no conflicts of interest in the authorship or publication of this contribution.

References

  1. 1.
    Zhang J, Liu P, Zhang F, Song Q (2018) Cloudnet: ground-based cloud classification with deep convolutional neural network. Geophys Res Lett 45(16):8665–8672Google Scholar
  2. 2.
    Xu C, Xu L, Gao Z, Zhao S, Zhang H, Zhang Y, Du X, Zhao S, Ghista D, Liu H (2018) Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture. Med Image Anal 50:82–94CrossRefGoogle Scholar
  3. 3.
    Gao Z, Xiong H, Liu X, Zhang H, Ghista D, Wu W, Li S (2017) Robust estimation of carotid artery wall motion using the elasticity-based state-space approach. Med Image Anal 37:1–21CrossRefGoogle Scholar
  4. 4.
    Gao Z, Li Y, Sun Y, Yang J, Xiong H, Zhang H, Liu X, Wu W, Liang D, Li S (2017) Motion tracking of the carotid artery wall from ultrasound image sequences: a nonlinear state-space approach. IEEE Trans Med Imaging 37(1):273–283CrossRefGoogle Scholar
  5. 5.
    Pollefeys M, Nister D, Frahm J, Akbarzedeh A, Mordohai P, Clipp B, Engels C, Gallup D, Kim S, Merrel P, Salmi C, Sinha S, Talton B, Wang L, Yang Q, Stewenius H, Yang R, Welch G, Towles H (2008) Detailed real-time urban 3D reconstruction from video. Int J Comput Vis 78(2–3):143–167CrossRefGoogle Scholar
  6. 6.
    Hu X, Mordohai P (2012) A quantitative evaluation of confidence measures for stereo vision. IEEE Trans Pattern Anal Mach Intell 34(11):2121–2133CrossRefGoogle Scholar
  7. 7.
    Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis 47(1/2/3):7–42CrossRefzbMATHGoogle Scholar
  8. 8.
    Zitnick C, Kang S (2007) Stereo for image-based rendering using image over-segmentation. Int J Comput 75(1):49–65Google Scholar
  9. 9.
    Claus A, Sormann A, Kamer K (2006) Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: IEEE international conference on pattern recognition. IEEE, pp 15–18Google Scholar
  10. 10.
    Yang Q, Wang L, Ahuja N (2010) A constant-space belief propagation algorithm for stereo matching. In: IEEE International Conference on Computer Vision and Pattern Recognition, IEEE, pp 1458-1465Google Scholar
  11. 11.
    Birchfield S, Tomasi C (1998) A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans Pattern Anal Mach Intell 20(4):401–406CrossRefGoogle Scholar
  12. 12.
    Hirschmuller H (2008) Stereo processing by semiglobal matching and mutual relationship. IEEE Trans Pattern Anal Mach Intell 30(2):328–341CrossRefGoogle Scholar
  13. 13.
    Clancar G, Kristan M, Karba R (2004) Wide-angle camera distortions and nonuniform illumination in mobile robot tracking. Robot Auton Syst 46(2):125–133CrossRefGoogle Scholar
  14. 14.
    Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: European conference on computer vision. Springer, pp 151–158Google Scholar
  15. 15.
    Sloan KR, Tanimoto SL (1979) Progressive refinement of raster images. IEEE Trans Comput C-28(11):871–874CrossRefGoogle Scholar
  16. 16.
    Hartley RI (1999) Theory and practice of projective rectification. Int J Comput Vis 35(2):115–127CrossRefGoogle Scholar
  17. 17.
    Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):43–77CrossRefGoogle Scholar
  18. 18.
    Szeliski R (1999) Prediction error as a quality metric for motion and stereo. In: IEEE international conference on computer vision. IEEE, pp 781–788Google Scholar
  19. 19.
    Wang Z, Zheng Z (2008) A region based stereo matching algorithm using cooperative optimization. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8Google Scholar
  20. 20.
    Nalpantidis L, Gasteratos A (2010) Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image Vis Comput 28(6):940–951CrossRefGoogle Scholar
  21. 21.
    Olague G, Fernandez F, Pérez C, Lutton E (2006) The infection algorithm: an artificial epidemic approach for dense stereo correspondence. Artif Life 12(4):593–615CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Medical TechnologyZhejiang Chinese Medical UniversityHangzhouChina

Personalised recommendations