Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81801
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dc.contributor.authorWang, Chenxingen
dc.contributor.authorQian, Kemaoen
dc.contributor.authorDa, Feipengen
dc.date.accessioned2016-07-29T08:23:45Zen
dc.date.accessioned2019-12-06T14:40:45Z-
dc.date.available2016-07-29T08:23:45Zen
dc.date.available2019-12-06T14:40:45Z-
dc.date.copyright2016en
dc.date.issued2016en
dc.identifier.citationWang, C., Qian, K., & Da, F. (2016). Regenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patterns. Optics and Lasers in Engineering, in press.en
dc.identifier.issn0143-8166en
dc.identifier.urihttps://hdl.handle.net/10356/81801-
dc.description.abstractFringe patterns are often produced from optical metrology. It is important yet challenging to reduce noise and remove a complicated background in a fringe pattern, for which empirical mode decomposition based methods have been proven useful. However, the mode-mixing problem and the difficulty in automatic mode classification limit the application of these methods. In this paper, a newly developed method named regenerated phase-shifted sinusoids assisted empirical mode decomposition is introduced to decompose a fringe pattern, and subsequently, a new noise-signal-background classification strategy is proposed. The former avoids the mode-mixing problem appearing during the decomposition, while the latter adaptively classifies the decomposition results to remove the noise and background. The proposed method is testified by both simulation and real experiments, which shows effective and robust for fringe pattern analysis under different noise, fringe modulation, and defects.en
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en
dc.language.isoenen
dc.relation.ispartofseriesOptics and Lasers in Engineeringen
dc.rights© 2016 Elsevier.en
dc.subjectImage analysisen
dc.subjectPhase retrievalen
dc.titleRegenerated phase-shifted sinusoids assisted EMD for adaptive analysis of fringe patternsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.contributor.researchMulti-plAtform Game Innovation Centreen
dc.identifier.doi10.1016/j.optlaseng.2016.04.018en
dc.identifier.rims192744en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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