Journal of Signal Processing Systems

, Volume 90, Issue 1, pp 157–164 | Cite as

Linear-Time Computation of Indexing Based Stereo Correspondence for Cameras with Automatic Gain Control

  • Vilson Heck Junior
  • Maurício E. Stivanello
  • Marcelo R. Stemmer


This paper is a contribution on the field of passive sparse stereo vision, specially for mobile robots navigation. A linear-time computing stereo matching algorithm based on indexing is discussed and improved for cameras with automatic gain control. Integral images and changes on data structures are used to achieve the goals. The method is evaluated by quantitative results utilizing Middlebury stereo datasets and it is able to achieve near 15 fps on a single thread process running on a Intel Core™ i7 without any SIMD use.


Sparse stereo correspondence Linear-time computation Indexing based Automatic gain control 


  1. 1.
    Bay, H, Tuytelaars, T, & Gool, LV (2006). Surf: speeded up robust features. In A. Leonardis, H. Bischof, & A. Pinz (Eds.), Proceedings 9th European conference on computer vision, (Vol. 3951 pp. 404–417). Berlin: Springer.Google Scholar
  2. 2.
    Bradski, G, & Kaehler, A (2008). Learning OpenCV: computer vision with the OpenCV library (1st edn). O’Reilly Media.Google Scholar
  3. 3.
    de Oliveira, M.A.F, & Wazlavick, RS (2005). Linear complexity stereo matching based on region indexing. In Proceedings of the XVIII Brazilian symposium on computer graphics and image processing—SIBGRAPI’05 (pp. 181–188). IEEE Computer Society.Google Scholar
  4. 4.
    Ding, J, Liu, J, Zhou, W, Yu, H, Wang, Y, & Gong, X (2011). Real-time stereo vision system using adaptive weight cost aggregation approach. EURASIP Journal on Image and Video Processing, V2011(1), 20–39. doi: 10.1186/1687-5281-2011-20. Scholar
  5. 5.
    Gardiman, RQ (2011). Visão estéreo com correspondência esparsa com features extraídos pelo método surf. Master’s thesis, Universidade Federal do Rio Grande do Norte.Google Scholar
  6. 6.
    Hirschmuller, H, & Scharstein, D (2007). Evaluation of cost functions for stereo matching. In IEEE conference on computer vision and pattern recognition, 2007 (pp. 1–8). CVPR ’07. IEEE. doi: 10.1109/CVPR.2007.383248.
  7. 7.
    Juan, L, & Gwon, O (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing (IJIP), 3(4), 143–152.Google Scholar
  8. 8.
    Lopez-Franco, M, Sanchez, EN, Alanis, AY, & López-Franco, C (2016). Neural control for driving a mobile robot integrating stereo vision feedback. Neural Processing Letters, 43(2), 425–444.CrossRefGoogle Scholar
  9. 9.
    Qu, Y, Jiang, J, Deng, X, & Zheng, Y (2014). Robust local stereo matching under varying radiometric conditions. IET Computer Vision, 8(4), 263–276. doi: 10.1049/iet-cvi.2013.0117.CrossRefGoogle Scholar
  10. 10.
    Satnik, A, Hudec, R, Kamencay, P, Hlubik, J, & Benco, M (2016). A comparison of key-point descriptors for the stereo matching algorithm. In 2016 26th international conference radioelektronika (RADIOELEKTRONIKA) (pp 292–295). doi: 10.1109/RADIOELEK.2016.7477419.
  11. 11.
    Scharstein, D, Pal, C, & 2007. Learning conditional random fields for stereo. In IEEE conference on computer vision and pattern recognition, 2007. CVPR ’07 (pp. 1–8). IEEE. doi: 10.1109/CVPR.2007.383191.
  12. 12.
    Scharstein, D, & Szeliski, R (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47, 7–42.CrossRefMATHGoogle Scholar
  13. 13.
    Scharstein, D, & Szeliski, R (2003). High-accuracy stereo depth maps using structured light. In IEEE computer society conference on computer vision and pattern recognition (vol 1, pp. 195–202). IEEE Computer Society.Google Scholar
  14. 14.
    Tippetts, B, Lee, D J, Lillywhite, K, & Archibald, J (2016). Review of stereo vision algorithms and their suitability for resource-limited systems. Journal of Real-Time Image Processing, 11(1), 5–25.CrossRefGoogle Scholar
  15. 15.
    Tola, E, Lepetit, V, & Fua, P (2008). A fast local descriptor for dense matching. In Proceedings of computer vision and pattern recognition. Alaska.Google Scholar
  16. 16.
    Tola, E, Lepetit, V, & Fua, P (2010). DAISY: an efficient dense descriptor applied to wide baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5), 815–830.CrossRefGoogle Scholar
  17. 17.
    Viola, P, & Jones, M J (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.CrossRefGoogle Scholar
  18. 18.
    Xue, B, Cao, L, Han, D, Bai, X, Zhou, F, & Jiang, Z (2016). A {DAISY} descriptor based multi-view stereo method for large-scale scenes. Journal of Visual Communication and Image Representation, 35, 15–24.CrossRefGoogle Scholar
  19. 19.
    Zhou, X, & Boulanger, P (2012). Radiometric invariant stereo matching based on relative gradients. In 2012 19th IEEE international conference on image processing (ICIP) (pp. 2989–2992).Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Instituto Federal de Santa CatarinaFlorianópolisBrazil
  2. 2.Universidade Federal de Santa CatarinaFlorianópolisBrazil

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