Scene learning, recognition and similarity detection in a fuzzy ontology via human examples
This addresses scene understanding for robots, but it appears incremental as it builds on existing fuzzy logic methods without major breakthroughs.
The paper tackles the problem of scene learning, recognition, and similarity detection by introducing a Fuzzy Logic framework that uses human examples to handle vagueness in spatial relations and represent scenes in a hierarchical fuzzy ontology, with results demonstrated through use cases.
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.