object recognition, belief networks, image motion analysis, learning (artificial intelligence), trained network, human-object-object-interaction affordance learning approach, HOO, motion models, object recognition reliability, paired objects, humans actions, object labels, Bayesian network
Digital Object Identifier (DOI)
This paper presents a novel human-object-object (HOO) interaction affordance learning approach that models the interaction motions between paired objects in a human-object-object way and use the motion models to improve the object recognition reliability. The innate interaction-affordance knowledge of the paired objects is modeled from a set of labeled training data that contains relative motions of the paired objects, humans actions, and object labels. The learned knowledge of the pair relationship is represented with a Bayesian Network and the trained network is used to improve recognition reliability of the objects.
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Citation / Publisher Attribution
2013 IEEE Workshop on Robot Vision (WORV), Clearwater Beach, FL, 2013, p. 1-6.
Scholar Commons Citation
Ren, Shaogang and Sun, Yu, "Human-Object-Object-Interaction Affordance" (2013). Computer Science and Engineering Faculty Publications. 84.