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Features are important components of object recognition systems. The combination of color and depth information given by RGB-D cameras provides opportunities for the development of improved features. In this talk, I will discuss our recent work on learning features for object recognition. Kernel descriptors provide a flexible framework for incorporating manually designed point features. Hierarchical matching pursuit uses sparse coding to learn features from raw, unlabeled RGB-D data. Both approaches achieve high accuracy on RGB-D object recognition tasks.

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