Optimization of identification of micro-objects based on the use of characteristics of images and properties of models

Abstract

A methodology has been developed for optimizing the identification of micro-objects based on the use of dynamic models, neural networks (NN) of various topologies, synthesis of mechanisms for extracting statistical, dynamic, specific characteristics of images, selecting and segmenting a contour, selecting reference points, reducing redundant points, and setting variables. Identification mechanisms based on statistical relationships, many points and dynamics of change, formalization of the coordinate matrices of distorted points, approximations during the deformation of a sequence of segments of stationary contour sections are proposed. A comparative analysis of the effectiveness of tools for preliminary image processing, recognition, and classification on the examples of pollen grains is carried out. Modified component circuits, adaptive learning algorithms of the Kohonen NN. A software package for visualization, recognition, classification of images of pollen grains was developed, a hybrid identification model was implemented with non-linear effects of factors and conditions of a priori insufficiency and uncertainty of parameters. The implemented software package is based on a three-layer NN of forwarding and backward propagation, learning algorithms with and without a teacher, a modified Kohonen network with vector quantization, clustering, segmentation, and the formation of a “sliding window”. The mechanisms of image identification in the presence of “noise”, error filtering, and neural network approximation of the contour curve of micro-objects images are investigated.