Sign language recognition using model-based tracking and a 3D Hopfield neural network
Machine Vision and Applications
IEEE Transactions on Visualization and Computer Graphics
Evolutionary Artificial Neural Network Design and Training for wood veneer classification
Engineering Applications of Artificial Intelligence
Context-independent phoneme recognition using a K-Nearest Neighbour classification approach
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
An OOPR-based rose variety recognition system
Engineering Applications of Artificial Intelligence
Design of an automatic wood types classification system by using fluorescence spectra
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fuzzy rule classifier: Capability for generalization in wood color recognition
Engineering Applications of Artificial Intelligence
Research and Implement of Chinese Text Classifier Based on Naïve Bayes Method
SKG '10 Proceedings of the 2010 Sixth International Conference on Semantics, Knowledge and Grids
Artificial neural network approach to authentication of coins by vision-based minimization
Machine Vision and Applications
Representing and recognizing objects with massive local image patches
Pattern Recognition
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybrid fuzzy control of robotics systems
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
Classifying tropical wood species poses a considerable economic challenge and failure to classify the wood species accurately can have significant effects on timber industries. The problem of wood recognition is compounded with the nonlinearities of the features among the similar wood species. Besides that, large wood databases presented a problem of large processing time especially for online wood recognition system. In view of these problems, we propose the use of fuzzy logic-based pre-classifier as a means of treating uncertainty to improve the classification accuracy of tropical wood recognition system. The pre-classifier serve as a clustering mechanism for the large database simplifying the classification process making it more efficient. The use of the fuzzy logic-based pre-classifier has managed to increase the accuracy of the wood recognition system by 4 % and reduce the processing time for training and testing by more than 75 % and 26 % respectively.