Automatic Segmentation by the Method of Interval Fusion with Preference Aggregation When Recognizing Weld Defects; Russian Journal of Nondestructive Testing; Vol. 59, iss. 12

Automatic Segmentation by the Method of Interval Fusion with Preference Aggregation When Recognizing Weld Defects; Russian Journal of Nondestructive Testing; Vol. 59, iss. 12

Bibliographic Details
Parent link:Russian Journal of Nondestructive Testing=Дефектоскопия.— .— New York: Springer Science+Business Media LLC.
Vol. 59, iss. 12.— 2023.— P. 1280-1290
Main Author: Muravyov (Murav’ev) S. V. Sergey Vasilyevich
Corporate Author: Томский политехнический университет
Other Authors: Nguyen Duc C.
Summary:Title screen
Quality control in welding is usually carried out during the visual inspection process and is highly dependent on an operator experience. In this paper, an approach to automatic detection and classification of a defective region is proposed, in which the segmentation of the analyzed photographic image of a weld (i.e., its division into defective and defect-free regions) is performed using the region growing procedure. The starting points for this procedure are selected by the authors’ robust method of interval fusion with preference aggregation (IF&PA) on the base of image histogram analysis. Testing the proposed approach for real life photographic images showed its ability to detect different types of weld defects with higher accuracy compared to traditional methods, such as the Otsu method and k-means.
Текстовый файл
AM_Agreement
Language:English
Published: 2023
Subjects:
image processing
segmentation
histogram
defect region
welding joint
interval fusion
preference aggregation
электронный ресурс
труды учёных ТПУ
Online Access:https://doi.org/10.1134/S1061830923600855
Статья на русском языке
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674997

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