Variational Bayesian Subgroup Adaptive Sparse Component Extraction for Diagnostic Imaging System

Bin Gao, Peng Lu, Wai Lok Woo, Gui Yun Tian, Yuyu Zhu, Martin Johnston

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)8142-8152
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Issue number10
Early online date5 Feb 2018
Publication statusPublished - Oct 2018

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