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• W1573897183 abstract "Recently introduced RGB-D cameras are capable of providing high quality synchronized videos of both color and depth. With its advanced sensing capabilities, this technology represents an opportunity to dramatically increase the capabilities of object recognition. It also raises the problem of developing expressive features for the color and depth channels of these sensors. In this paper we introduce hierarchical matching pursuit (HMP) for RGB-D data. HMP uses sparse coding to learn hierarchical feature representations from raw RGB-D data in an unsupervised way. Extensive experiments on various datasets indicate that the features learned with our approach enable superior object recognition results using linear support vector machines." @default.
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• W1573897183 date "2013-01-01" @default.
• W1573897183 modified "2023-10-03" @default.
• W1573897183 title "Unsupervised Feature Learning for RGB-D Based Object Recognition" @default.
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• W1573897183 doi "https://doi.org/10.1007/978-3-319-00065-7_27" @default.
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