Skip to main content
Springer Nature Link
Log in

Stereo Matching with Improved Radiometric Invariant Matching Cost and Disparity Refinement

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9771))

Included in the following conference series:

Abstract

Accurate and real-time stereo correspondence is a pressing need for many computer vision applications. In this paper, an improved radiometric invariant matching cost algorithm is proposed. It effectively combines modified census transform with relative gradients measures. Although it is very simple, comparison results on Middlebury stereo testbed demonstrate that it has much lower error rates than many existing algorithms and is very close to the ANCC algorithm which represents the current state of the art under extreme luminance condition but outperforms the ANCC algorithm greatly when there are small radiometric distortions. In addition, we also develop a disparity refinement method with computational complexity invariant to the disparity range. Experimental results on Middlebury datasets show those artifacts near object boundaries are reduced using the proposed disparity refinement method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    For gray input image, \( W_{p,q} (I) = \exp ( - |I(p) - I(q)|/\delta ) \).

  2. 2.

    Note that since this paper is not to evaluate cost aggregation algorithm, the Middlebury 2014 datasets which contain several new features are not used for evaluation.

  3. 3.

    For implementing BT, we used the code provided in [11].

  4. 4.

    For the runtime of ANCC, we direct use the results reported in [21].

References

  1. Heo, Y.S., Lee, K.M., Lee, S.U.: Robust stereo matching using adaptive normalized cross-correlation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 807–822 (2011)

    Article  Google Scholar 

  2. Kim, S., Ham, B., Kim, B., Sohn, K.: Mahalanobis distance cross-correlation for illumination-invariant stereo matching. IEEE Trans. Circ. Syst. Video Technol. 24(11), 1844–1859 (2014)

    Article  Google Scholar 

  3. Zhou, X., Boulanger, P.: Radiometric invariant stereo matching based on relative gradients. In: 19th 9th IEEE International Conference on Image Processing, pp. 2989–2992. IEEE (2001)

    Google Scholar 

  4. Viola, P., Wells, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vis. 24(2), 137–154 (1997)

    Article  Google Scholar 

  5. Fife, W., Archibald, J.: Improved census transforms for resource-optimized stereo vision. IEEE Trans. Circ. Syst. Video Technol. 23(1), 60–73 (2013)

    Article  Google Scholar 

  6. Sinha, S., Scharstein, D., Szeliski R.: Efficient high-resolution stereo matching using local plane sweeps. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1582–1589. IEEE (2014)

    Google Scholar 

  7. Bleyer, M., Rother, C., Kohli, P., Scharstein D., Sinha S.: Object stereo-joint stereo matching and object segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3081–3088. IEEE (2011)

    Google Scholar 

  8. Gu, Z., Su, X., Liu, Y., Zhang, Q.: Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recogn. Lett. 29, 1230–1235 (2008)

    Article  Google Scholar 

  9. Hosni, A., Bleyer, M., Gelautz, M., Rhemann, C.: Local stereo matching using geodesic weights. In: 19th IEEE International Conference on Image Processing, pp. 2093–2096. IEEE (2009)

    Google Scholar 

  10. Rhemann, C., Hosni, A., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3017–3024. IEEE (2011)

    Google Scholar 

  11. Yang, Q.: A non-local cost aggregation method for stereo matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1402–1409. IEEE (2012)

    Google Scholar 

  12. Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., Zhang, X.: On building an accurate stereo matching system on graphics hardware. In: IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 467–474. IEEE (2011)

    Google Scholar 

  13. Sun, X., Mei, X., Jiao, S., Zhou, M., Wang, H.: Stereo matching with reliable disparity propagation. In: International Conference on 3D Imaging, Modeling, Processing, Visualization, Transmission, pp. 132–139, IEEE (2011)

    Google Scholar 

  14. Yang, Q., Ji, P., Li, D., Yao, S., Zhang, M.: Fast stereo matching using adaptive guided filtering. Image Vis. Comput. 32(3), 202–211 (2014)

    Article  Google Scholar 

  15. Huang, X., Cui, G., Zhang, Y.: A fast non-local disparity refinement method for stereo matching. In: IEEE International Conference on Image Processing, pp. 3823–3827. IEEE (2014)

    Google Scholar 

  16. Yang, Q.: Local smoothness enforced cost volume regularization for fast stereo correspondence. IEEE Signal Process. Lett. 22(9), 1429–1433 (2015)

    Article  Google Scholar 

  17. Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. Pattern Anal. Mach. Intell. 20(4), 401–406 (1998)

    Article  Google Scholar 

  18. Xu, L., Au, O.C., Sun, W., Fang, L., Zou, F., Li, J.: Stereo matching with optimal local adaptive radiometric compensation. IEEE Signal Process. Lett. 22(2), 131–135 (2015)

    Article  Google Scholar 

  19. Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994)

    Google Scholar 

  20. Miron, A., Ainouz, S., Rogozan, A., Bensrhair, A.: A robust cost function for stereo matching of road scenes. Pattern Recogn. Lett. 38, 70–77 (2014)

    Article  Google Scholar 

  21. Mouats, T., Aouf, N., Richardson, M.: A novel image representation via local frequency analysis for illumination invariant stereo matching. IEEE Trans. Image Process. 24(9), 2685–2700 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported by a grant from National Natural Science Foundation of China (NSFC, No. 61504032).

Author information

Authors and Affiliations

  1. Microelectronics Center, Harbin Institute of Technology, Harbin, 150000, China

    Jinjin Shi, Fangfa Fu, Yao Wang, Weizhe Xu & Jinxiang Wang

Authors
  1. Jinjin Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fangfa Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weizhe Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinxiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinxiang Wang .

Editor information

Editors and Affiliations

  1. Tongji University , Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari , Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong , North Wollongong, New South Wales, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Shi, J., Fu, F., Wang, Y., Xu, W., Wang, J. (2016). Stereo Matching with Improved Radiometric Invariant Matching Cost and Disparity Refinement. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science(), vol 9771. Springer, Cham. https://doi.org/10.1007/978-3-319-42291-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42291-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42290-9

  • Online ISBN: 978-3-319-42291-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics