Phase selection of synthetic discriminant function filters

Computation of arbitrarily constrained synthetic discriminant functions

An algorithm for computing correlation filters based on synthetic discriminant functions that can be displayed on current spatial light modulators is presented. The procedure is nondivergent, computationally feasible, and capable of producing multiple solutions, thus overcoming some of the pitfalls of previous methods.

Automatic spatial frequency selection algorithm for pattern recognition by correlation

We describe an approach to compute filters that automatically performs a spatial frequency selection to improve interclass discrimination and to reduce intraclass sensitivity. This approach is achieved by using as input to the filter synthesis a set of reference images to compute the filters and a set of distorted images to introduce the distortion or noise model of the reference images. Simulation results of correlation examples are provided for two pattern-recognition problems and are compared with the ones obtained with the standard minimum average correlation energy filters.

Tutorial survey of composite filter designs for optical correlators

A tutorial survey is presented of the many composite filter designs proposed for distortion-invariant optical pattern recognition. Remarks are made throughout regarding areas for further investigation.

Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency

Filters that have optimal trade-offs among the criteria of noise robustness, sharpness of the correlation peak, and Horner efficiency are presented, and an explicit mathematical expression is provided. Owing to their optimality, these filters provide a figure of merit and then permit a rigorous characterization of filter performances for optical pattern recognition.