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

Leonid Bilevich*, Leonid Yaroslavsky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationReal-Time Image and Video Processing 2010
StatePublished - 2010
EventReal-Time Image and Video Processing 2010 - Brussels, Belgium
Duration: 16 Apr 201016 Apr 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceReal-Time Image and Video Processing 2010


Dive into the research topics of 'Fast algorithms for computing image local statistics in windows of arbitrary shape and weights'. Together they form a unique fingerprint.

Cite this