Optimization of Identification of Images of Micro-Objects Taking Into Account Systematic Error Based on Neural Networks

Abstract

A methodology has been developed for optimizing the identification of micro-objects based on the use of neural networks (NN) of various topologies, synthesis of image processing mechanisms, extracting statistical, dynamic, specific characteristics, selecting and segmenting a contour, selecting reference points and reducing redundant points, taking into account systematic error factors, choosing an adequate model, setting variables and optimization. Methods and algorithms for determined and multivariate analysis, obtaining the coefficients of influence and elasticity of factors, approximating the contours represented by time series are proposed. Modified component schemes of the NN, training algorithms, developed a software package (SP) for visualization, recognition, classification of images of pollen grains, implemented a hybrid identification model taking into account the non-linearity of the effects of factors under the condition of a priori insufficiency and uncertainty of parameters. The efficiency of the SP was studied on the basis of a three-layer NN of forward and backward propagation of errors, learning algorithms with and without a teacher, Kohonen network with procedures for vector quantization, clustering and segmentation and the formation of a sliding windows. The results of image identification in the presence of noise, optimization based on filtering systematic error and NN extrapolation of the trend of the contour curve of the images of pollen grains were obtained.