Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System

Gao, Bin, Lu, Peng, Woo, Wai Lok and Tian, G. Y. (2018) Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System. In: ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, 15th - 20th April 2018, Calgary, Canada.

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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:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Diagnostic imaging system, Low-rank decomposition, Sparse decomposition, Variational Bayes
Subjects:	G400 Computer Science J500 Materials Technology not otherwise specified
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	02 Apr 2019 10:30
Last Modified:	10 Oct 2019 20:47
URI:	http://nrl.northumbria.ac.uk/id/eprint/38684

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