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- W2014307168 abstract "In order for robots to function in unstructured environments in interaction with humans, they must be able to reason about the world in a semantic meaningful way. An essential capability is to segment the world into semantic plausible object hypotheses. In this paper we propose a general framework which can be used for reasoning about objects and their functionality in manipulation activities. Our system employs a hierarchical segmentation framework that extracts object hypotheses from RGB-D video. Motivated by cognitive studies on humans, our work leverages on contextual information, e.g., that objects obey the laws of physics, to formulate object hypotheses from regions in a mathematically principled manner." @default.
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- W2014307168 date "2014-08-01" @default.
- W2014307168 modified "2023-09-25" @default.
- W2014307168 title "Unsupervised object exploration using context" @default.
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- W2014307168 doi "https://doi.org/10.1109/roman.2014.6926302" @default.
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