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In the task of finding the features of digital images, it is relevant to determine solutions that provide high processing speed. In the article experimental studies of the application of differential invariants based on the partial image model as a linear combination of B-splines that are close to the average interpolation. Such a model retains the properties of the Gaussian model in the frequency domain, but has less computational complexity, which allows us to better investigate its asymptotic properties and the properties of the corresponding partial derivatives used in the construction of differential invariants. The issue of differences in gradient magnitude, Lapsasian, Hessian determinant, and curvature of the scaling curve during image processing was studied, and the masks of low-frequency filters and operator masks were minimized based on differences of smoothing operators. It has been experimentally proved that smoothing of digital images and reduction of their linear sizes allows to formalize the process of feature selection on the basis of analysis of probability distributions of introduced differential invariants. The suggested approach may be recommended when searching for similar objects containing different images. The approach considered in the work has a low computational complexity, which makes it possible to recommend it for use in systems with a low computation speed, in particular for systems operating on single-board computers.

COMPARATIVE ANALYSIS OF DIFFERENTIAL INVARIANTS BASED ON THE SPLINE MODEL FOR VARIOUS IMAGE DISTORTION