| DC Field | Value | Language |
|---|
| dc.contributor.author | Серебряная, Л. В. | - |
| dc.contributor.author | Бочкарев, К. Ю. | - |
| dc.contributor.author | Попитич, А. Я. | - |
| dc.date.accessioned | 2019-11-15T09:23:04Z | - |
| dc.date.available | 2019-11-15T09:23:04Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Серебряная, Л. В. Модель автоматической классификации и локализации образов = Model of Automatic Classification and Localization of Images / Л. В. Серебряная, К. Ю. Бочкарев, А. Я. Попитич // Цифровая трансформация. – 2019. – № 1(6). – С. 43–48. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/37260 | - |
| dc.description.abstract | Работа посвящена идентификации образов на изображениях, которая выполняется в результате процедур классификации и локализации. Предложена архитектура сверточной искусственной нейронной сети, позволяющая решать как задачу классификации, так и задачу локализации образов. Комбинированная модель показала приемлемые результаты в ходе решения обеих задач. Все параметры для работы сети определяются автоматически с помощью генетического алгоритма. | ru_RU |
| dc.language.iso | ru | ru_RU |
| dc.publisher | ГИАЦ | ru_RU |
| dc.subject | цифровая трансформация | ru_RU |
| dc.subject | нейронные сети | ru_RU |
| dc.subject | генетические алгоритмы | ru_RU |
| dc.title | Модель автоматической классификации и локализации образов | ru_RU |
| dc.title.alternative | Model of Automatic Classification and Localization of Images | - |
| dc.type | Статья | ru_RU |
| local.description.annotation | The work is devoted to the identification of images in pictures, which is performed as a result of
the classification and localization procedures. Analysis of models, methods and algorithms has shown that for solving
the set task it is preferable to use machine learning, an artificial neural network and a genetic algorithm. The architecture
of a convolutional artificial neural network is proposed. It can solve both the problem of classification and the problem
of localizing images. First the network is trained, then a class is determined for the image fed to its input. Objects are
localized in the image at the final stage of operations of the convolutional neural network. For this, the output values
of the penultimate layer of the model are analyzed, after which the layers are traversed in the reverse order. Its goal
is to find the regions with the highest response on the source image. The combined model showed acceptable results
both in classification and in localization of objects. All parameters for the network are determined automatically
using a genetic algorithm. Further improvement of the proposed model results will be performed by implementing
distributed computing on it. | - |
| Appears in Collections: | №1(6)
|
