Wood Defects Detection by Using Texture Analysis

Abstract

Machine vision based inspection system are in great focus nowadays for quality control applications. The proposed work presents a novel approach for classification of wood knot defects for an automated inspection. The proposed technique utilizes gray level co-occurrence matrix and laws texture energy measures as texture feature extractors and feedforward back propagation neural network as classifier. The proposed work involves the comparison of gray level co-occurrence matrix based features with laws texture energy measures based features. Firstly it takes contrast, correlation, energy and homogeneity as input parameters to a feedforward back propagation neural network to predict wood defects and then it take energy calculated from laws texture energy measures based energy maps as input feature to a feedforward back propagation neural network. The mean squared error (MSE) for training data is found to be 0.0718 and 90.5 % overall average classification accuracy is achieved when laws texture energy measures based features are used as input to the neural network as compared to gray level co-occurrence matrix based input features where MSE for training data is found to be 0.10728 and 84.3 % overall average classification accuracy is achieved. The proposed technique shows promising results to classify wood defects using a feed forward back propagation neural network.