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dc.contributor.author강민구-
dc.contributor.author장훈석-
dc.contributor.author윤건우-
dc.contributor.authorMuhammad Tariq Mahmood-
dc.date.accessioned2021-06-09T04:23:24Z-
dc.date.available2021-06-09T04:23:24Z-
dc.date.issued2020-01-
dc.identifier.issn--
dc.identifier.other53829-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/70452-
dc.description.abstractShape from Focus (SFF) is one of passive optical methods for estimating 3D shape of an object. In SFF, a large number of 2D images with different focus levels are required. The number of images may affect the complexity and the accuracy of the results. In this manuscript, a Gaussian process regression (GPR) method is proposed to get 3D shape from the minimum number of 2D images. The proposed method (SFF.GPR) is applied to fit focus curves, which are obtained by applying one of focus measure operators. Experimental results demonstrate the effectiveness of the proposed method.-
dc.publisherIEEE ICCE 2020-
dc.subjectShape from Focus-
dc.subjectGaussian Process Regression-
dc.titleOptimal Sampling for Shape from Focus by Using Gaussian Process Regression-
dc.typeConference Paper-
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KIST Publication > Conference Paper
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