Uncertainty modelling using Dempster-Shafer theory for improving detection of weld defects

被引：44

作者：

Kaftandjian, V

Dupuis, O

Babot, D

Zhu, YM

机构：

[1] INSA, CNDRI, F-69621 Villeurbanne, France

[2] CREATIS, CNRS, UMR 5515, F-69621 Villeurbanne, France

来源：

PATTERN RECOGNITION LETTERS | 2003年 / 24卷 / 1-3期

关键词：

modelling; image segmentation; X-ray imaging; weld inspection; Dempster-Shafer theory;

D O I：

10.1016/S0167-8655(02)00276-3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an approach that is based on the combined use of Dempster-Shafer (DS) theory and fuzzy sets for improving automatic detection of weld defects. It consists in modelling detection uncertainty in feature space through using the mass function weighted by membership degrees, and fusing the features of objects using DS combination rule. The method is demonstrated on the typical industrial application of weld inspection. The obtained results show that, by modelling detection uncertainty, a confidence level can be associated to each detected object, making the defect detection more precise and reliable. (C) 2002 Elsevier Science B.V. All rights reserved.

引用

页码：547 / 564

页数：18

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