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Proceedings Paper

Input image spectral density estimation for real-time adaption of correlation filters for optical pattern recognition
Author(s): Anders Grunnet-Jepsen; Sylvie G. Tonda; Vincent Laude
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Paper Abstract

The problem of image noise estimation for improved noise robustness and discrimination capabilities of optical correlation filters is discussed. Colored noise is often used in the literature as an approximation to the true noise spectral density in the input image of a correlator. This conjecture is verified on different kinds of input images, i.e. their power spectral densities are fitted to a colored noise model. The quality of the resulting approximation is discussed. It is then shown that incorporating this noise estimation into optimal trade-off filters can significantly improve both the discrimination capabilities and the signal to noise ratio of the resulting adaptive correlation filter above that of the classical filters for which the noise parameters are not estimated. Although its performances are in general found to be markedly inferior to those of true nonlinear filtering techniques that are optimal for adaptive image correlation, the proposed adaptive method is attractive in terms of computation time. The optical implementation of the proposed method is also presented.

Paper Details

Date Published: 27 December 1996
PDF: 6 pages
Proc. SPIE 2969, Second International Conference on Optical Information Processing, (27 December 1996); doi: 10.1117/12.262628
Show Author Affiliations
Anders Grunnet-Jepsen, Thomson-CSF (France)
Sylvie G. Tonda, Thomson-CSF (France)
Vincent Laude, Thomson-CSF (France)

Published in SPIE Proceedings Vol. 2969:
Second International Conference on Optical Information Processing
Zhores I. Alferov; Yuri V. Gulyaev; Dennis R. Pape, Editor(s)

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