Abstract
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.
Similar content being viewed by others
References
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.
Bradski, G, & Kaehler, A (2008). Learning OpenCV: computer vision with the OpenCV library (1st edn). O’Reilly Media.
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.
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. http://jivp.eurasipjournals.com/content/2011/1/20.
Gardiman, RQ (2011). Visão estéreo com correspondência esparsa com features extraídos pelo método surf. Master’s thesis, Universidade Federal do Rio Grande do Norte.
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.
Juan, L, & Gwon, O (2009). A comparison of sift, pca-sift and surf. International Journal of Image Processing (IJIP), 3(4), 143–152.
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.
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.
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.
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.
Scharstein, D, & Szeliski, R (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47, 7–42.
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.
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.
Tola, E, Lepetit, V, & Fua, P (2008). A fast local descriptor for dense matching. In Proceedings of computer vision and pattern recognition. Alaska.
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.
Viola, P, & Jones, M J (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.
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.
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Junior, V.H., Stivanello, M.E. & Stemmer, M.R. Linear-Time Computation of Indexing Based Stereo Correspondence for Cameras with Automatic Gain Control. J Sign Process Syst 90, 157–164 (2018). https://doi.org/10.1007/s11265-017-1228-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-017-1228-8