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.
Original language | English |
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Pages (from-to) | 8142-8152 |
Number of pages | 11 |
Journal | IEEE Transactions on Industrial Electronics |
Volume | 65 |
Issue number | 10 |
Early online date | 5 Feb 2018 |
DOIs | |
Publication status | Published - Oct 2018 |
Keywords
- Diagnostic imaging system
- electromagnetic thermography
- low-rank decomposition
- sparse decomposition
- variational Bayesian (VB)