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

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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 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 languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Pages1518-1522
Number of pages5
ISBN (Electronic)9781538646588, 9781538646571
ISBN (Print)9781538646595
DOIs
Publication statusPublished - 13 Sep 2018
Event2018 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 201820 Apr 2018
https://2018.ieeeicassp.org/default.asp

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Volume2018-April
ISSN (Electronic)2379-190X

Conference

Conference2018 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18
Internet address

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