Variational Bayesian Subgroup Adaptive Sparse Component Extraction for Diagnostic Imaging System

Gao, Bin, Lu, Peng, Woo, Wai Lok, Tian, Gui Yun, Zhu, Yuyu and Johnston, Martin (2018) Variational Bayesian Subgroup Adaptive Sparse Component Extraction for Diagnostic Imaging System. IEEE Transactions on Industrial Electronics, 65 (10). pp. 8142-8152. ISSN 0278-0046

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Official URL: http://dx.doi.org/10.1109/TIE.2018.2801809

Abstract

A novel unsupervised sparse component extraction algorithm is proposed for detecting micro defects while 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 subsparse 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.

Item Type: Article
Uncontrolled Keywords: Diagnostic imaging system, electromagnetic thermography, low-rank decomposition, sparse decomposition, variational Bayesian (VB)
Subjects: G400 Computer Science
H300 Mechanical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 02 Apr 2019 10:09
Last Modified: 02 Apr 2019 10:09
URI: http://nrl.northumbria.ac.uk/id/eprint/38683

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