Texture segmentation using multi-layered backpropagation

The authors trained the multi-layered backpropagation neural network to segment two paper samples with very similar paper formation characteristics. The paper samples were chosen deliberately in order to evaluate the multi-layered backpropagation performance in a difficult classification problem. The authors used the texture features obtained from the spatial gray-tone dependence cooccurrence matrices as inputs to the multi-layered backpropagation network. Results show good classification percentages when compared to a subjective evaluation method.<<ETX>>