TREE-STRUCTURED TEMPORAL INFORMATION FOR FAST HISTOGRAM COMPUTATION

Séverine Dubuisson

2010

Abstract

In this paper we present a new method for fast histogram computing. Based on the known tree-representation histogram of a region, also called reference histogram,, we want to compute the one of another region. The idea consists in computing the spatial differences between these two regions and encode it to update the histogram. We never need to store complete histograms, except the reference image one (as a preprocessing step). We compare our approach with the well-known integral histogram, and obtain better results in terms of processing time while reducing the memory footprint. We show theoretically and with experimental results the superiority of our approach in many cases. Finally, we demonstrate the advantage of this method on a visual tracking application using a particle filter by improving its time computing.

References

  1. Adam, A., Rivlin, E., and Shimshoni, I. (2006). Robust fragments-based tracking using the integral histogram. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 798-805.
  2. Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by probability distributions. Bulletin of the Calcutta Mathematical Society, 35:99-110.
  3. Caselles, V., Lisani, J., Morel, J., and Sapiro, G. (1999). Shape preserving local histogram modification. IEEE Trans. on Image Processing, 8(2):220-230.
  4. Chen, Z. (2003). Bayesian filtering: From kalman filters to particle filters, and beyond. Technical report, McMaster University.
  5. Gevers, T. (2001). Robust histogram construction from color invariants. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26:113-118.
  6. Gordon, N. J., Salmond, D. J., and Smith, A. F. M. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, 140(2):107-113.
  7. Halawani, A. and Burkhardt, H. (2005). On using histograms of local invariant features for image retrieval. In IAPR Conference on Machine Vision Applications, pages 538-541.
  8. Isard, M. and Blake, A. (1998). Condensation - conditional density propagation for visual tracking. International Journal of Computer Vision, 29:5-28.
  9. Pérez, P., Hue, C., Vermaak, J., and Gangnet, M. (2002). Color-based probabilistic tracking. In ECCV 7802: Proceedings of the 7th European Conference on Computer Vision-Part I, pages 661-675, London, UK. Springer-Verlag.
  10. Perreault, S. and Hebert, P. (2007). Median filtering in constant time. IEEE Trans. on Image Processing, 16(9):2389-2394.
  11. Porikli, F. (2005). Integral histogram: A fast way to extract histograms in cartesian spaces. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 829-836.
  12. Sizintsev, M., Derpanis, K. G., and Hogue, A. (2008). Histogram-based search: A comparative study. in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 1-8.
  13. Tang, G., Yang, G., and Huang, T. (1979). A fast twodimensional median filtering algorithm. In IEEE Transactions on Acoustics, Speech and Signal Processing, pages 13-18.
  14. Vermaak, J., Godsill, S. J., and Pérez, P. (2004). Monte carlo filtering for multi-target tracking and data association. IEEE Transactions on Aerospace and Electronic Systems, 41:309-332.
  15. Viola, P. and Jones, M. (2001). Robust real-time object detection. In International Journal of Computer Vision.
  16. Wang, H., Suter, D., Schindler, K., and Shen, C. (2007). Adaptive object tracking based on an effective appearance filter. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9):1661-1667.
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Paper Citation


in Harvard Style

Dubuisson S. (2010). TREE-STRUCTURED TEMPORAL INFORMATION FOR FAST HISTOGRAM COMPUTATION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 13-22. DOI: 10.5220/0002815800130022


in Bibtex Style

@conference{visapp10,
author={Séverine Dubuisson},
title={TREE-STRUCTURED TEMPORAL INFORMATION FOR FAST HISTOGRAM COMPUTATION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={13-22},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002815800130022},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - TREE-STRUCTURED TEMPORAL INFORMATION FOR FAST HISTOGRAM COMPUTATION
SN - 978-989-674-029-0
AU - Dubuisson S.
PY - 2010
SP - 13
EP - 22
DO - 10.5220/0002815800130022