Pattern recognition with an adaptive generalized SDF filter

摘要

Most of captured images present degradations due to blurring and additive noise; moreover objects of interest can be geometrically distorted. The classical methods for pattern recognition based on correlation are very sensitive to intensity degradations and geometric distortions In this work, we propose an adaptive generalized filter based on synthetic discriminant function (SDF). With the help of computer simulation we analyze and compare the performance of the adaptive correlation filter with that of common correlation filters in terms of discrimination capability and accuracy of target location when input scenes are degraded and a target is geometrically distorted.