Abstract
In this paper, we present a new approach for quickly computing the histograms of a set of unrotating rectangular regions. Although it is related to the well-known Integral Histogram (IH), our approach significantly outperforms it, both in terms of memory requirements and of response times. By preprocessing the region of interest (ROI) computing and storing a temporary histogram for each of its pixels, IH is effective only when a large amount of histograms located in a small ROI need be computed by the user. Unlike IH, our approach, called Min-Space Integral Histogram, only computes and stores those temporary histograms that are strictly necessary (less than 4 times the number of regions). Comparative tests highlight its efficiency, which can be up to 75 times faster than IH. In particular, we show that our approach is much less sensitive than IH to histogram quantization and to the size of the ROI.
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Tang, G., Yang, G., Huang, T.: A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech and Signal Processing 27, 13–18 (1979)
Perreault, S., Hebert, P.: Median filtering in constant time. IEEE Transactions on Image Processing 16, 2389–2394 (2007)
Sizintsev, M., Derpanis, K., Hogue, A.: Histogram-based search: A comparative study. In: Proc. of CVPR, pp. 1–8 (2008)
Porikli, F.: Integral histogram: A fast way to extract histograms in Cartesian spaces. In: Proc. of CVPR, pp. 829–836 (2005)
Gordon, N., Salmond, D., Smith, A.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings of Radar and Signal Processing 140, 107–113 (1993)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: Proc. of CVPR, pp. 798–805 (2006)
Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: Proc. of CVPR, pp. 983–990 (2009)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: Proc. of ICCV (2009)
Erdem, E., Dubuisson, S., Bloch, I.: Fragment based tracking with adaptive cue integration. Computer Vision and Image Understanding 116, 827–841 (2012)
Dubuisson, S.: Tree-structured image difference for fast histogram and distance between histograms computation. Pattern Recognition Letters 32, 411–422 (2011)
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Dubuisson, S., Gonzales, C. (2012). Min-Space Integral Histogram. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7573. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33709-3_14
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DOI: https://doi.org/10.1007/978-3-642-33709-3_14
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