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

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


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© 2018 IEEE. A novel unsupervised sparse component extraction algorithm for diagnosing micro defects in thermography imaging system is presented. The approach is optimized under Variational Bayesian framework, which is fully automated and does not require manual selection of the parameters in the solution. An internal sub sparse grouping mechanism and adaptive fine-tuning have been built into the proposed algorithm to control the sparsity. The proposed method is used to automatically detect the micro defects on metals. Other contending defect feature extraction and sparse pattern extraction methods are employed for comparison. The algorithm has been shown to improve the detection precision of both artificial and natural cracks.

Publication metadata

Author(s): Gao B, Lu P, Woo WL, Tian GY

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Year of Conference: 2018

Pages: 1518-1522

Online publication date: 13/09/2018

Acceptance date: 15/04/2018

Publisher: IEEE


DOI: 10.1109/ICASSP.2018.8461749

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538646588