Lp-TV model for structure extraction with end-to-end contour learning

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dc.contributor.authorSong, Chunwei
dc.contributor.authorMaiseli, Baraka J.
dc.contributor.authorZuo, Wangmeng
dc.contributor.authorGao, Huijun
dc.date.accessioned2019-05-05T11:42:07Z
dc.date.available2019-05-05T11:42:07Z
dc.date.issued2017
dc.description.abstractStructure extraction is important for human perception. However, for various textured images, computers can hardly achieve this goal. Despite a plethora of studies to address the challenge, results from most previous methods contain unwanted artifacts and over-smoothed structures. Therefore, to address the weaknesses, we have proposed a variational model with end-to-end contour learning capability. Our formulation dwells in two observations: likelihood for representation of residual textures may be well abstracted using super Gaussian distribution, and edge metrics with semantic meaning may benefit structure preservation. The augmented Lagrangian method is adopted for optimal computation. Compared with classical approaches, our method offers a higher performance in structure extraction, including situations where the images have significant nonuniformity of the scale features.en_US
dc.identifier.doi10.1109/IECON.2017.8217241
dc.identifier.isbn978-1-5386-1127-2
dc.identifier.urihttp://hdl.handle.net/20.500.11810/5196
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectImage edge detectionen_US
dc.subjectComputational modelingen_US
dc.subjectTVen_US
dc.subjectSemanticsen_US
dc.subjectGaussian distributionen_US
dc.subjectMathematical modelen_US
dc.subjectAdaptation modelsen_US
dc.titleLp-TV model for structure extraction with end-to-end contour learningen_US
dc.typeConference Paperen_US
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