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

Bin Gao; Peng Lu; W. L. Woo; G. Y. Tian

doi:10.1109/ICASSP.2018.8461749

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

Bin Gao, Peng Lu, W. L. Woo, G. Y. Tian

Computer and Information Sciences Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

5 Citations (Scopus)

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
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	IEEE
Pages	1518-1522
Number of pages	5
ISBN (Electronic)	9781538646588, 9781538646571
ISBN (Print)	9781538646595
DOIs	https://doi.org/10.1109/ICASSP.2018.8461749
Publication status	Published - 13 Sept 2018
Event	2018 IEEE International Conference on Acoustics, Speech and Signal Processing: Signal Processing and Artificial Intelligence: Changing the World - Calgary Telus Convention Center, Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018 https://2018.ieeeicassp.org/default.asp

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher	IEEE
Volume	2018-April
ISSN (Electronic)	2379-190X

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated title	ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18
Internet address	https://2018.ieeeicassp.org/default.asp

Keywords

Diagnostic imaging system
Low-rank decomposition
Sparse decomposition
Variational Bayes

Access to Document

10.1109/ICASSP.2018.8461749

Cite this

Gao, B., Lu, P., Woo, W. L., & Tian, G. Y. (2018). Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 1518-1522). (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April). IEEE. https://doi.org/10.1109/ICASSP.2018.8461749

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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.",

keywords = "Diagnostic imaging system, Low-rank decomposition, Sparse decomposition, Variational Bayes",

author = "Bin Gao and Peng Lu and Woo, {W. L.} and Tian, {G. Y.}",

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address = "United States",

note = "2018 IEEE International Conference on Acoustics, Speech and Signal Processing : Signal Processing and Artificial Intelligence: Changing the World, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

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Gao, B, Lu, P, Woo, WL & Tian, GY 2018, Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2018-April, IEEE, pp. 1518-1522, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8461749

Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System. / Gao, Bin; Lu, Peng; Woo, W. L. et al.
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE, 2018. p. 1518-1522 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Gao, Bin

AU - Lu, Peng

AU - Woo, W. L.

AU - Tian, G. Y.

PY - 2018/9/13

Y1 - 2018/9/13

N2 - 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.

AB - 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.

KW - Diagnostic imaging system

KW - Low-rank decomposition

KW - Sparse decomposition

KW - Variational Bayes

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M3 - Chapter

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T2 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing

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ER -

Gao B, Lu P, Woo WL, Tian GY. Variational Bayes Sub-Group Adaptive Sparse Component Extraction for Diagnostic Imaging System. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. IEEE. 2018. p. 1518-1522. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8461749

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

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