Identifying defects on plywood using a minimum distance classifier and a neural network

packianather — Thu, 23 Jun 2005 14:40:48 +0000

This paper describes the application of a minimum distance classifier and a neural network in identifying defects on plywood. The performance achieved by these two classifiers in this study has been used to compare the two methods for classification tasks. While the neural network misclassification fell to 13.5% that of the minimum distance classifier remained at 37% showing the superiority of the neural network. This is due to its inherent ability to deal with nonlinearity and create soft decision boundaries to separate pattern classes, thus making it a more efficient and intelligent classifier.

Innovative Production Machines and Systems Conference - minimum distance classifier

Identifying defects on plywood using a minimum distance classifier and a neural network