No abstract

This paper focuses on a Fuzzy Reasoning Classification Method to improve the potential of pattern recognition in the automated inspection and classification of wooden boards. After the definition of the characteristic features, we implement a fuzzy inference mechanism allowing to take into account the subjectivity of the human visual system. In this article, we have decided to work on the distribution and the representation of the Information. In this sense, our study speaks about the impact of fuzzification on the recognition rates and the structure of our decision module. The part concerning the classification mechanism allows ourselves to integrate knowledge in the generation of the numeric model. This knowledge is enquired at the different field experts thanks to the NIAM formalism. The results, which are presented on a generic benchmark and real data, show the efficiency of such an approach.

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