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.
CITATION STYLE
Seib, V., Wojke, N., Knauf, M., & Paulus, D. (2015). Detecting fine-grained affordances with an anthropomorphic agent model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8926, pp. 413–419). Springer Verlag. https://doi.org/10.1007/978-3-319-16181-5_30
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