Abstract
The application of a regularization technique to filter synthesis in pattern recognition with synthetic discriminant function filters is presented. The proposed technique uses the stabilizing functional approach for two-dimensional ill-posed problems. Filter synthesis is thus formulated as the minimization of some relevant criteria with specified correlation values for some training input images and limited maximum value of a stabilizing functional. The choice of a particular stabilizing functional to be minimized is related to a priori knowledge regarding the pattern-recognition problem. The analogy between the regularization methods and optimal trade-off filters is also presented and is illustrated with numerical experiments.
© 1994 Optical Society of America
Full Article | PDF ArticleMore Like This
Abhijit Mahalanobis, B. V. K. Vijaya Kumar, Sewoong Song, S. R. F. Sims, and J. F. Epperson
Appl. Opt. 33(17) 3751-3759 (1994)
Z. Bahri and B. V. K. Vijaya Kumar
J. Opt. Soc. Am. A 5(4) 562-571 (1988)
David Casasent
Appl. Opt. 23(10) 1620-1627 (1984)