One of the most perspective types of invariant correlation filters are LPCC (Linear Phase Coefficient Composite) filters. Application of the given type of filters allows the receiving of invariance of correlation peak to various types of distortions of object. In the given paper results of studies LPCC filters designed for various types of correlators are discussed. Results of synthesis of filters and calculations of correlations of images with the constructed filters are presented in comparison.

This paper presents the results of computer simulations of pattern recognition using LPCC filters, designed for various types of distortions of input images (rotation, change of scale and rotation + change of scale).

This paper present the results of computer simulations of pattern recognition using LPCC filters, designed for various types of distortions of input images (rotation, change of scale and rotation + change of scale).

One of the widespread methods for distorted patterns is to use a distortion invariant correlation filters. Invariant filters have different properties that are quite good for different pattern recognition problems. This paper presents the results of computer simulations of pattern recognition using different modern approaches on distortion invariant correlation filters. The different types of correlation filters (MACE, GMACE, LPCCF, WBKF and others) are compared for input test sets of different examples of patterns. There are presented results of pattern recognition for different types of distortions. The output correlation peaks are compared by its characteristics. The obtained results of comparison provide that in some cases there are correspondences between the choused correlation filter, the variant of pattern and type of the distortion for optimal output peak characteristics.