Inverse correlation filters of objects features with optimized regularization for image processing

The problem of extraction of the image objects features by means of using the inverse filters (IF) is considered. The IF are formed by the inversion of the matrix composed of correlation vectors of a set of objects templates examples. The inversion is made with the help of singular value decomposition. Three approaches to regularization and its impact on IF recognition properties are also considered. There was defined the functional that specifies minimal mutual relations between functions of the filters to obtain optimal separation of the features. A training process is used in order to obtain filters with high recognition performance.