Methodology of Optimization of Identification of the Contour and Brightness-Color Picture of Images of Micro-Objects

Abstract

A methodology has been developed for optimizing the identification and processing of images of pollen grains based on the use of mechanisms for extracting texture, specific characteristics, and geometric features of micro-objects. A technique is proposed for the trend description of the contour according to the characteristics of the growth of a dynamic curve based on a wide range of functional dependencies. Extrapolation mechanisms have been developed for exponential, autoregressive, moving average, adaptive smoothing, statistical models of forecasting and linking of reference points of the image contour. The principles of masking reference points, pixel representation, adjusting their width, modifying biquadratic interpolation, as well as mechanisms for segmenting and detecting changes in the dynamics of the contour curve are proposed. The mechanisms for describing a contour curve by an interpolation spline - the Daubechies function, 4, 8 orthogonal polynomials in order to identify the characteristics of pollen grains - are investigated. A set of programs has been implemented that focuses on the application of a mechanism for reducing zero points, regulating the beginning, center, segment boundaries, mask width of the reference point, and color-brightness picture. The program modules of the complex are created in C++ and tested in the «CUDA parallel computing environment.