Proceedings Article | 5 May 2010
KEYWORDS: Diamond, Image processing, Digital filtering, Image enhancement, Composites, Image segmentation, Digital image processing, Detection and tracking algorithms, Image compression, Image restoration
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.
