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Proceedings Paper

Fast algorithms for computing image local statistics in windows of arbitrary shape and weights
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Paper Abstract

Computing image local statistics is required in many image processing applications such as local adaptive image restoration, enhancement, segmentation, target location and tracking, to name a few. These computations must be carried out in sliding window of a certain shape and weights. Generally, it is a time consuming operation with per-pixel computational complexity of the order of the window size, which hampers real-time applications. For acceleration of computations, recursive computational algorithms are used. However, such algorithms are available only for windows of certain specific forms, such as rectangle and octagon, with uniform weights. We present a general framework of fast parallel and recursive computation of image local statistics in sliding window of almost arbitrary shape and weights with "per-pixel" computational complexity that is substantially of lower order than the window size. As an illustration of this framework, we describe methods for computing image local moments such as local mean and variance, image local histograms and local order statistics (in particular, minimum, maximum, median), image local ranks, image local DFT, DCT, DcST spectra in polygon-shaped windows as well as in windows with non-uniform weights, such as Sine lobe, Hann, Hamming and Blackman windows.

Paper Details

Date Published: 4 May 2010
PDF: 9 pages
Proc. SPIE 7724, Real-Time Image and Video Processing 2010, 772405 (4 May 2010); doi: 10.1117/12.854657
Show Author Affiliations
Leonid Bilevich, Tel Aviv Univ. (Israel)
Leonid Yaroslavsky, Tel Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 7724:
Real-Time Image and Video Processing 2010
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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