Please help transcribe this video using our simple transcription tool. You need to be logged in to do so.
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.
Questions and AnswersYou need to be logged in to be able to post here.