Methods have been developed to optimize the identification of pollen grains using statistical, dynamic, textural and specific image characteristics. Mechanisms for point and nonlinear verification of the correspondence of the contours of the input and reference objects - pollen grains, as well as for adjusting the parameters of raster images have been studied and proposed. Implemented mechanisms for reducing contour zero points, reducing raster dimensions, scaling, threshold and level control, encoding and placing images of micro-objects based on a pyramidal model, selecting contour reference points, cognitive analysis, searching for points with annealing, prohibition, based on stochastic modeling using a truncated chain Markova. A set of programs for identification, recognition, classification and systematization of pollen grains was implemented in C++ in the parallel computing environment “CUDA”.
| Mualliflar | Жуманов, Исраил, Сафаров, Рустам |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2024-10-09 |
| Jild | 7 |
| Son | 3 |
| Betlar | 92-98 |
| Til | Rus |
| DOI | 10.62132/ijdt.v7i3.201 |
DOI: 10.62132/ijdt.v7i3.201 · Maqolaning asl sahifasi
идентификация, изображение, пыльцевое зерно, распознавание, классификация, эффективность, погрешность, трудоемкость, комплекс программ, identification, pollen grains, recognition, classification, efficiency, error, labor intensity, cost, software package
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