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Lookup NU author(s): Dr Bin Gao,
Dr Wai Lok Woo,
Professor Gui Yun TianORCiD,
Dr Martin Johnston
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.
For re-use rights please refer to the publisher's terms and conditions.
IEEE A novel unsupervised sparse component extraction algorithm is proposed for detecting micro defects when employing a thermography imaging system. The proposed approach is developed using the Variational Bayesian framework. This enables a fully automated determination of the model parameters and bypasses the need for human intervention in manually selecting the appropriate image contrast frames. An internal sub-sparse grouping mechanism and adaptive fine-tuning strategy have been built to control the sparsity of the solution. The proposed algorithm is computationally affordable and yields a high accuracy objective performance. Experimental tests on both artificial and natural defects have been conducted to verify the efficacy of the proposed method.
Author(s): Gao B, Lu P, Woo WL, Tian GY, Zhu Y, Johnston M
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Industrial Electronics
Print publication date: 01/10/2018
Online publication date: 05/02/2018
Acceptance date: 02/04/2016
Date deposited: 27/02/2018
ISSN (print): 0278-0046
ISSN (electronic): 1557-9948
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