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• W1969624871 abstract "We address the glass object localization problem with a RGB-D camera. Our approach uses a nonparametric, data-driven label transfer scheme for local glass boundary estimation. A weighted voting scheme based on a joint feature manifold is adopted to integrate depth and appearance cues, and we learn a distance metric on the depth-encoded feature manifold. Local boundary evidence is then integrated into a MRF framework for spatially coherent glass object detection and segmentation. The efficacy of our approach is verified on a challenging RGB-D glass dataset where we obtained a clear improvement over the state-of-the-art both in terms of accuracy and speed." @default.
• W1969624871 created "2016-06-24" @default.
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• W1969624871 date "2013-09-01" @default.
• W1969624871 modified "2023-10-18" @default.
• W1969624871 title "Glass object segmentation by label transfer on joint depth and appearance manifolds" @default.
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• W1969624871 doi "https://doi.org/10.1109/icip.2013.6738606" @default.
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