Papers › Scene learning, recognition and similarity detection in a fuzzy ontology via human examples
Scene learning, recognition and similarity detection in a fuzzy ontology via human examples
Luca Buoncompagni, Fulvio Mastrogiovanni, Alessandro Saffiotti
This paper introduces a Fuzzy Logic framework for scene learning, recognition and similarity detection, where scenes are taught via human examples. The framework allows a robot to: (i) deal with the intrinsic vagueness associated with determining spatial relations among objects; (ii) infer similarities and dissimilarities in a set of scenes, and represent them in a hierarchical structure represented in a Fuzzy ontology. In this paper, we briefly formalize our approach and we provide a few use cases by way of illustration. Nevertheless, we discuss how the framework can be used in real-world scenarios.
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