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Balovsyak S.V., Derevyanchuk O.V., Derevianchuk Ya.V., Tomash V.V., Yarema S.V. Segmentation of railway transport images using fuzzy logic // Trans Motauto World. Vol. 7 (2022), Issue 3, pg(s) 122-125.
| dc.contributor.author | Balovsyak, Serhiy Vasyl'ovych | |
| dc.contributor.author | Derevyanchuk, Oleksandr Volodymyrovych | |
| dc.contributor.author | Yarema, Sergiy Volodymyrovych | |
| dc.contributor.author | Tomash, Vasyl Vasylyovych | |
| dc.contributor.author | Derevianchuk, Yaroslav Volodymyrovych | |
| dc.date.accessioned | 2022-11-19T17:19:13Z | |
| dc.date.available | 2022-11-19T17:19:13Z | |
| dc.date.issued | 2022 | |
| dc.identifier.issn | PRINT 2367-8399 | |
| dc.identifier.issn | WEB 2534-8493 | |
| dc.identifier.uri | https://archer.chnu.edu.ua/xmlui/handle/123456789/5810 | |
| dc.description.abstract | A prototype of a system for segmenting images of trains and wagons has been developed. Video cameras and specialized websites are used as the source of the original images. Median filtering of images and increase of their local contrast is carried out. The contours of the objects were calculated using the Sobel and Canny methods. Image segmentation is performed by the method of contour lines. As a result of the processing on the images of trains and wagons, meaningful areas (segments) were identified, for example, windows, headlights, etc. Detection of content areas of the object is performed using fuzzy membership functions. The hardware and software implementation of the computer system is made in Python using scipy and scikit-fuzzy libraries, the Google Colab cloud platform and Raspberry Pi 3B+ microcomputer. | uk_UA |
| dc.subject | DIGITAL VIDEO CAMERA, PYTHON, IMAGE SEGMENTATION, LOCAL CONTRAST, FUZZY LOGIC. | uk_UA |
| dc.title | Balovsyak S.V., Derevyanchuk O.V., Derevianchuk Ya.V., Tomash V.V., Yarema S.V. Segmentation of railway transport images using fuzzy logic // Trans Motauto World. Vol. 7 (2022), Issue 3, pg(s) 122-125. | uk_UA |
| dc.type | Thesis | uk_UA |
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