Scientific and methodological foundations, and methods for identification, recognition, and classification of micro-objects in the systems of palynology, ecology, and medicine have been developed based on the use of tools for tracking, detection, and correction of point distortions, filtering, segmentation, and transformation under conditions of a priori insufficiency, parametric uncertainty, and low reliability of image processing.  Constructive approaches, principles, and models have been developed that are aimed at constructing algorithms for tools that use the characteristics of components in the structure of micro-objects - statistical, dynamic, morphological, histological, and signal points of the image. Algorithms for determining the optimal frequency, sampling step, and model parameters aimed at implementing a tool for correcting distortions in a sequence of points of the contour of a micro-object image have been implemented. Identification algorithms based on Daubechies 4-discrete wavelet transform and tools for synchronizing signal points of the damped sine, bell-shaped, rectangular pulse type in the presence of interference and other image defects have been implemented. A software package for visualization, identification, recognition, classification, and systematization of images of micro-objects – pollen grains in palynological systems – has been developed and applied in solving problems based on real measurements.