Detecting Fine-Grained Affordances with an Anthropomorphic Agent Model

In this paper we propose an approach to distinguish affordances on a fine-grained scale. We define an anthropomorphic agent model and parameterized affordance models. The agent model is transformed according to affordance parameters to detect affordances in the input data. We present first results on distinguishing two closely related affordances derived from sitting. The promising results support our concept of fine-grained affordance detection.

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