Learning fuzzy concepts for machine vision

This paper discusses a technique that effectively combines conceptual graph theory with fuzzy logic for 3D object recognition. The suitability of fuzzy conceptual graphs for learning continuous valued features is brought out with a machine vision system that employs model-based object recognition. The object recognition system learns instances of segmented range objects from different views, represented in terms of fuzzy conceptual graphs as a memory aggregate. Recognition is done by deriving a conceptual graph for a query and performing graph matching. The technique is found suitable for continuous valued concepts and hence used as with a powerful capability for learning