Anchor-diagonal-based shape adaptive local support region for efficient stereo matching


Local stereo algorithms are preferred for real-time applications due to their computational efficiency. Deciding the size of the required local support region is a challenging task. It fails to estimate accurate disparity for small support region and introduces fattening effect for big support region. Hence, a shape adaptive local support region is necessary to achieve accurate disparity. This paper proposes an anchor-diagonal-based shape adaptive support region construction for stereo matching. The proposed algorithm dynamically constructs local support region, and the aggregated matching cost is used for Normalized Cross-Correlation-based similarity measure. The algorithm is evaluated using benchmarked Middlebury stereo evaluation, and the obtained disparities are efficient as compared to state-of-the-art methods.

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  1. 1.

    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. J. Comput. Vis. 47(1), 7–42 (2002)

    Article  MATH  Google Scholar 

  2. 2.

    Fusiello, A., Roberto, V., Trucco, E.: Efficient stereo with multiple windowing. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 858–863. San Juan, Puerto Rico (1997)

  3. 3.

    Kang, S.B., Szeliski, R., Chai, J.: Handling occlusions in dense multi-view stereo. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 103–110. Kauai Hawaii (2001)

  4. 4.

    Veksler, O.: Stereo correspondence with compact windows via minimum ratio cycle. IEEE Trans. PAMI. 24(12), 1654–1660 (2002)

    Article  Google Scholar 

  5. 5.

    Veksler, O.: Fast variable window for stereo correspondence using integral images. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 556–561. Wisconsin (2003)

  6. 6.

    Lu, J., Lafruit, G., Catthoor, F.: Anisotropic local high-confidence voting for accurate stereo correspondence. In: Proceedings SPIE-IST Electronic, Imaging, vol. 6812, pp. 605822-1–605822-10 (2008)

  7. 7.

    Xu, Y., Wang, D., Feng, T., Shum, H.Y.: Stereo computation using radial adaptive windows. In: Proceedings IEEE Conference on Pattern Recognition, vol. 3, pp. 595–598. Canada (2002)

  8. 8.

    Yoon, K.J., Kweon, S.: Adaptive support-weight approach for correspondence search. IEEE Trans. PAMI 28(4), 650–656 (2006)

    Google Scholar 

  9. 9.

    Tombari, F., Mattoccia, S., Stefano, L.D.: Segmentation based adaptive support for accurate stereo correspondence. In: Proceedings of PSIVT, pp. 427–438. Springer, LNCS-4872, Santiago, Chile (2007)

  10. 10.

    Zhang, K., Lu, J., Lafruit, G.: Cross-based local stereo matching using orthogonal integral images. IEEE Trans. Circuits Syst. Video Technol. 19(7), 1073–1079 (2009)

    Article  Google Scholar 

  11. 11.

    Kim, H.S., Yoo, J.M. Park, M.S., Dinh, T.N., Lee, G.S.: An anisotropic diffusion based on diagonal edges. In: IEEE International Conference on Advanced Communication Technology, pp. 384–388. Japan (2007)

  12. 12.

    Edirisinghe, E.A., Bedi, S.: Gradient-based predictor for diagonal edge pixels in JPEG-LS. IEEE Electron. Lett. 37(22), 1327–1328 (2001)

    Article  Google Scholar 

  13. 13.

    Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. IEEE Trans. PAMI 31(9), 1582–1599 (2009)

    Article  Google Scholar 

  14. 14.

    Heo, Y.S., Lee, K.M., Lee, S.U.: Robust stereo matching using adaptive normalized cross-correlation. IEEE Trans. PAMI 33(4), 807–822 (2011)

    Article  MathSciNet  Google Scholar 

  15. 15.

    Scharstein, D., Szeliski, R.: Middlebury Stereo Vision Page (2008). [Online Accessed: March 2012]. Available:

  16. 16.

    Gu, Z., Su, X., Liu, Y., Zhang, Q.: Local stereo matching with adaptive support-weight, rank transform and disparity calibration. Pattern Recognit. Lett. 29(3), 1230–1235 (2008)

    Article  Google Scholar 

  17. 17.

    Wang, L., Liao, M., Gong, M., Yang, R., Nister, D.: High-quality real-time stereo using adaptive cost aggregation and dynamic programming. In: Proceedings 3D Data Processing, Visualization, and Transmission, pp. 798–805. North Corolina (2006)

  18. 18.

    Nalpantidis, L., Gasteratos, A.: Biologically and psychophysically inspired adaptive support weights algorithm for stereo correspondence. Robotics Auton. Syst. 58(5), 457–464 (2010)

    Article  Google Scholar 

  19. 19.

    El-Etriby, S., Al-Hamadi, A., Michaelis, B.: Dense stereo correspondence with slanted surface using phase-based algorithm. In: IEEE International Symposium on Industrial Electronics, pp. 1807–1813. Vigo Spain (2007)

  20. 20.

    El-Etriby, S., Al-Hamadi, A., Michaelis, B.: Dense depth map reconstruction by phase difference-based algorithm under influence of perspective distortion. Int. J. Mach. Graph. Vis. 15(3), 349–361 (2006)

    Google Scholar 

  21. 21.

    Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans. PAMI 30(6), 1068–1080 (2008)

    Article  Google Scholar 

  22. 22.

    Humenberger, M., Zinner, C., Weber, M., Kubinger, W., Vincze, M.: A fast stereo matching algorithm suitable for embedded real-time systems. Comput. Vis. Image Underst. 114(11), 1180–1202 (2010)

    Article  Google Scholar 

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This research is carried out under the scheme Structural PhD of Manipal University. Authors would like to thank Martin Humenberger from Austrian Institute of Technology, Vienna, Austria, for providing real-time stereo images.

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Correspondence to U. Raghavendra.

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Raghavendra, U., Makkithaya, K. & Karunakar, A.K. Anchor-diagonal-based shape adaptive local support region for efficient stereo matching. SIViP 9, 893–901 (2015).

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  • Stereo matching
  • Disparity estimation
  • Local support region