DocumentCode :
1032527
Title :
A statistical algorithm for efficient computation of correlations
Author :
Schils, George F.
Author_Institution :
Sandia Nat. Lab., Livermore, CA, USA
Volume :
40
Issue :
11
fYear :
1992
fDate :
11/1/1992 12:00:00 AM
Firstpage :
2857
Lastpage :
2863
Abstract :
A statistical method is employed to approximate the operation of correlation. By using Bernoulli sampling to generate the template or correlation mask, a sparse template can be produced. The sampling procedure is shown to produce an unbiased estimate of the correlation signal. The variance of the output signal is also evaluated. Various approximation accuracies can be obtained by proper design of the correlation template. Because the templates produced by this technique are binary and sparse, the correlation operation can be implemented very efficiently. It is shown that the computational complexity of this algorithm for implementing correlation is N2 (for images), where N is the linear dimension of the images. The technique is illustrated on an example
Keywords :
computational complexity; correlation theory; image processing; pattern recognition; signal processing; statistical analysis; Bernoulli sampling; computational complexity; correlation mask; correlation operation; correlation signal; image processing; output signal variance; pattern recognition; signal processing; sparse template; statistical algorithm; stochastic process; Autocorrelation; Finite impulse response filter; Frequency domain analysis; Multidimensional systems; Pattern recognition; Signal design; Signal processing algorithms; Signal sampling; Statistical analysis; Wiener filter;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.165680
Filename :
165680
Link To Document :
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