Advertisement

Distributed Averages of Gradients (DAG): A Fast Alternative for Histogram of Oriented Gradients

  • M. Hossein Mirabdollah
  • Mahmoud A. Mohamed
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)

Abstract

We propose a compact descriptor for the purpose of dense image matching and object recognition. The descriptor is calculated based on local gradients about each point in an image. It contains the averages of gradients at four different windows surrounding a center point. The descriptor is calculated much faster than histogram of oriented gradients (HOG). Additionally, it will be shown that it is more discriminative than HOG. We used the new descriptor for two applications needed in RoboCup competitions very often. First, computation of dense optical flows and 3D scene reconstruction from two views. Second, human face detection.

References

  1. 1.
    Opencv feature descriptor comparison report (2015). http://computer-vision-talks.com/articles/2011-08-19-feature-descriptor-comparison-report/. Accessed 23 Mar 2016
  2. 2.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). CVIU 110(3), 346–359 (2008)Google Scholar
  3. 3.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24673-2_3CrossRefGoogle Scholar
  4. 4.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15561-1_56CrossRefGoogle Scholar
  5. 5.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of CVPR, pp. 886–893 (2005)Google Scholar
  6. 6.
    Demetz, O., Hafner, D., Weickert, J.: The complete rank transform: a tool for accurate and morphologically invariant matching of structure. In: Proceedings of BMVC (2013)Google Scholar
  7. 7.
    Fan, B., Wu, F., Hu, Z.: Rotationally invariant descriptors using intensity order pooling. TPAMI 34(10), 2031–2045 (2012)CrossRefGoogle Scholar
  8. 8.
    Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. IJRR 1231–1237 (2013)Google Scholar
  9. 9.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Workshop on Generative-Model Based Vision CVPR (2004)Google Scholar
  10. 10.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: Proceedings of ICCV, pp. 2548–2555 (2011)Google Scholar
  11. 11.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Miksik, O., Mikolajczyk, K.: Evaluation of local detectors and descriptors for fast feature matching. In: Proceedings of ICPR, pp. 2681–2684 (2012)Google Scholar
  13. 13.
    Mirabdollah, M.H., Mertsching, B.: Fast techniques for monocular visual odometry. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 297–307. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-24947-6_24CrossRefGoogle Scholar
  14. 14.
    Mohamed, M.A., Rashwan, H.A., Mertsching, B., Garcia, M.A., Puig, D.: Illumination-robust optical flow using local directional pattern. IEEE Trans. Circ. Syst. Video Technol. 24, 1–9 (2014)CrossRefGoogle Scholar
  15. 15.
    Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: Proceedings of IV, pp. 401–407 (2012)Google Scholar
  16. 16.
    Rashwan, H.A., Mohamed, M.A., García, M.A., Mertsching, B., Puig, D.: Illumination robust optical flow model based on histogram of oriented gradients. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 354–363. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40602-7_38CrossRefGoogle Scholar
  17. 17.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of ICCV, pp. 2564–2571 (2011)Google Scholar
  18. 18.
    Shi, J., Tomasi, C.: Good features to track. In: Proceedings of CVPR, pp. 593–600 (1994)Google Scholar
  19. 19.
    Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: Proceedings of ICCV, pp. 603–610 (2011)Google Scholar
  20. 20.
    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).  https://doi.org/10.1007/BFb0028345CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • M. Hossein Mirabdollah
    • 1
  • Mahmoud A. Mohamed
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET LabUniversity of PaderbornPaderbornGermany

Personalised recommendations