TY - GEN

T1 - Fast algorithms for computing image local statistics in windows of arbitrary shape and weights

AU - Bilevich, Leonid

AU - Yaroslavsky, Leonid

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=77954607757&partnerID=8YFLogxK

U2 - 10.1117/12.854657

DO - 10.1117/12.854657

M3 - פרסום בספר כנס

AN - SCOPUS:77954607757

SN - 9780819481979

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Real-Time Image and Video Processing 2010

Y2 - 16 April 2010 through 16 April 2010

ER -