An appearance fuzzy sensor integrating a knowledge model

Emmanuel Schmitt (1, 2) , Vincent Bombardier (1) , Patrick Charpentier (1) , Raphaël Vogrig (2)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 LUXSCAN TECHNOLOGIES

This article exposes a modeling process of expert's knowledge to improve a system of wooden board appearance classification. The aesthetic criteria for the classification are the color and the texture. The extraction of knowledge concerning these two notions is realized with the Natural language Information Analysis Method (NIAM). Then, to improve the current industrial system, we suggest to use this symbolic knowledge model to generate a numeric model. This numeric part is built thanks to a Fuzzy Rules based Inference System (SIF). Fuzzy Sets Theory is here well adapted in order to obtain not-disjointed result classes and to manipulate subjective or symbolic information. Finally, we propose to create our appearance sensor under the form of a "fuzzy sensor".

Mots clés: Knowledge modelling; Fuzzy sensor; Fuzzy rules; Color; Texture

Domaines: Traitement du signal et de l'image [eess.SP]; Automatique / Robotique; Traitement du signal et de l'image [eess.SP]

Emmanuel Schmitt, Vincent Bombardier, Patrick Charpentier, Raphaël Vogrig. An appearance fuzzy sensor integrating a knowledge model. 12th IFAC Symposium on Information Control Problems in Manufacturing, INCOM'2006, May 2006, Saint-Etienne, France. pp.111-116. ⟨hal-00087472⟩