Minimum Variance SDF Design Using Adaptive Algorithms

A. Mahalanobis

doi:10.1117/12.949028

7 March 1989 Minimum Variance SDF Design Using Adaptive Algorithms

A. Mahalanobis

Proceedings Volume 1005, Optics, Illumination, and Image Sensing for Machine Vision III; (1989) https://doi.org/10.1117/12.949028
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

Abstract

The synthesis of minimum variance synthetic discriminant functions (MVSDF) for target detection in the presence of colored noise involves the invention of the noise correlation matrix. This may be numerically difficult even for low-resolution images. Methods must be found that allow synthesis of the MVSDF even when matrix inversion is not possible. We suggest an adaptive algorithm based on the well known LMS rule. Adaptive MVSDFs can maintain optimality in the presence of noise with unknown and non-stationary characteristics. The proposed adaptive synthesis technique "learns" characteristics of the noise source from sample realizations. This may seem as additional data and computation requirements, but comparable information and effort is necessary in the direct method to estimate the correlation matrix. The direct method (which uses the inverse of the correlation matrix) may be described as a two-step process, where the correlation matrix is determined in the first step and is used in the second step for the direct synthesis of the MVSDF. The proposed method iteratively converges on the optimum filter, and simultaneously gains knowledge about the noise source.

Citation Download Citation

A. Mahalanobis "Minimum Variance SDF Design Using Adaptive Algorithms", Proc. SPIE 1005, Optics, Illumination, and Image Sensing for Machine Vision III, (7 March 1989); https://doi.org/10.1117/12.949028

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KEYWORDS

Target detection

Image filtering

Machine vision

Matrices

Direct methods

Distortion

Detection and tracking algorithms

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