Toggle Main Menu Toggle Search

Open Access padlockePrints

Variational Bayesian Sub-group Adaptive Sparse Component Extraction for Diagnostic Imaging System

Lookup NU author(s): Dr Bin Gao, Dr Wai Lok Woo, Professor Gui Yun TianORCiD, Dr Martin JohnstonORCiD



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.

Publication metadata

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

Year: 2018

Volume: 65

Issue: 10

Pages: 8142-8152

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

Publisher: IEEE


DOI: 10.1109/TIE.2018.2801809


Altmetrics provided by Altmetric