In many applications such as the furniture and the wood panel production, the classification of wood kinds can provide relevant information concerning the aspect, the properties and the preparation procedures of the products. Usually, the wood kind classification is made by trained operators, but this solution suffers of important drawbacks: it is time consuming and it has low repeatability/accuracy since the classification is related to the operator experience and fatigue. In the literature, some attempts to solve this applicative problem by automatic systems are present, but, unfortunately, these solutions present complex measures and setups. In this paper, we present a novel approach for wood kinds classification based on a neural network system which exploits the emitted spectrum of the wood samples filtered with a bank of low-cost optical filters coupled with a set of photo detectors. The structure of the proposed system can be directly implemented in an embedded low-cost system. The results of the system simulations are very satisfactory and they demonstrate that this approach is feasible and very promising.
A low-cost neural-based approach for wood types classification / R. Donida Labati, M. Gamassi, F. Scotti, V. Piuri - In: Computational Intelligence for Measurement Systems and Applications, 2009. CIMSA '09. IEEE International Conference on[s.l] : IEEE, 2009 Jun 10. - ISBN 9781424438198. - pp. 199-203 (( convegno IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA tenutosi a Hong Kong nel 2009 [10.1109/CIMSA.2009.5069947].
A low-cost neural-based approach for wood types classification
R. Donida LabatiPrimo
;M. GamassiSecondo
;F. ScottiPenultimo
;V. PiuriUltimo
2009
Abstract
In many applications such as the furniture and the wood panel production, the classification of wood kinds can provide relevant information concerning the aspect, the properties and the preparation procedures of the products. Usually, the wood kind classification is made by trained operators, but this solution suffers of important drawbacks: it is time consuming and it has low repeatability/accuracy since the classification is related to the operator experience and fatigue. In the literature, some attempts to solve this applicative problem by automatic systems are present, but, unfortunately, these solutions present complex measures and setups. In this paper, we present a novel approach for wood kinds classification based on a neural network system which exploits the emitted spectrum of the wood samples filtered with a bank of low-cost optical filters coupled with a set of photo detectors. The structure of the proposed system can be directly implemented in an embedded low-cost system. The results of the system simulations are very satisfactory and they demonstrate that this approach is feasible and very promising.File | Dimensione | Formato | |
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