Scientific and methodological foundations for optimizing the processing of micro-objects, in particular, pollen grains, have been developed on the basis of models and methods of preliminary information processing with mechanisms for filtering, identifying, and using textural, specific characteristics, and geometric features of images. The efficiency of image filtering mechanisms is investigated based on the use of statistical control rules, adaptive two-threshold control, trend functions, and control of the contour description error, traditional detectors of random noise, and interference. The mechanisms of image identification using biquadratic, orthogonal algebraic polynomials, 4th order splines are proposed. A technique for optimizing image identification by finding a function by its integrals in a family of straight lines based on the Mellin and Fourier transforms has been developed. Combined models with mechanisms for smoothing reference points, segmentation of contours, filtering with detection of changes in dynamics, optimization by the conjugate gradient method, time relaxation have been built. A software complex for the recognition and classification of images of pollen grains in C ++ has been developed, which includes testing identification algorithms based on the Daubechies spline function, orthogonal algebraic polynomials of 4, 8 orders in the parallel computing environment “CUDA”.

A problem was formulated, and methods and algorithms for identifying, recognizing, and classifying micro-objects were developed using problem-oriented image processing systems utilizing the characteristics of image structural components, dynamic models, and neural networks. The micro-objects studied were pollen grains and unicellular microorganisms, the application of which is in demand in palynology, environmental protection, ecology, and medicine. Models, algorithms, and software were developed based on the statistical, dynamic, morphometric, textural, and brightness characteristics of point image signals. The main contribution of the work is a hybrid model combining Daubechies wavelet functions (orders 4 and 8) with convolutional neural networks, which provides an average pollen grain recognition and classification accuracy of up to 97.7% and an overall classification accuracy of 98.2%. For comparative evaluation, the YOLO11m model was used as an independent baseline deep learning model, achieving 88.6% overall accuracy on a validation set of 280 images. The software includes tools for Gaussian filtering, median filtering, Sobel and Canny operators, and threshold correction of defective points with hard and soft control rules. The software package includes modules for training a convolutional neural network. The study was conducted on real-world databases of micro-object images for crop breeding and environmental pollution assessment.

Constructive approaches, principles, and models for optimizing the identification of micro-objects have been developed based on the use of combined statistical, dynamic models and neural networks with mechanisms for filtering noise and foreign particles of images of medical objects and pollen grains. Algorithms for learning neural networks under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are investigated. The mechanisms of contour selection, segmentation, obtaining the boundaries of segments with hard and soft thresholds, filtering using the morphological features of the image have been developed [1]. Mechanisms for recognition and classification of images, adaptation of parameter values, tuning of the network structure, approximation and smoothing of random emissions, bursts in the image contour are proposed. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions are obtained for estimating the identification errors caused by nonstationarity, inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of micro-objects has been developed. The results were obtained for correct, incorrect recognition, as well as rejected pollen samples, which were synthesized with cubic, biquadratic, interpolation spline-functions and wavelet transforms.