A methodology has been developed for optimizing the identification, recognition, and classification of micro-objects using redundant image information structures - histological, morphological, fractal, correlation, and spectral characteristics. The following mechanisms are implemented: transformations, scaling, reconstruction of images of selection and segmentation of the contour; selection of control points on the contour, setting the values of variables, taking into account their statistical relationships, the dynamics of change; shift, rotation, deformation of a sequence of segments. Researched neural network architectures: 19-13-12; 30-40-12; 40-15-12; 35-15-12; 25-12-12 with learning algorithms for the neural network based on the Levenberg-Marquardt method. A software package for visualization, recognition, and classification of images of micro-objects is implemented, based on the use of mechanisms for extracting correlation and spectral characteristics, a model for adapting correlation windows, optimization, and modified training of a multilayer neural network.
| Mualliflar | I. Jumanov, R. Safarov, O. Djumanov |
|---|---|
| Jurnal | Samarqand davlat universiteti ilmiy tadqiqotlar axborotnomasi |
| Nashr sanasi | 2026-07-01 |
| Jild | 1 |
| Son | 3 |
| Betlar | 102-110 |
| DOI | 10.59251/2181-3973.2025.v1.138.1.3950 |
DOI: 10.59251/2181-3973.2025.v1.138.1.3950 · Maqolaning asl sahifasi
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