Wood defects classification using laws texture energy measures and supervised learning approach. (October 2017)

• Record Type: Journal Article
• Authors: Kamal, K.; Qayyum, R.; Mathavan, S.; Zafar, T.
• Is Part Of: Advanced engineering informatics. Volume 34(2017)
• Page Start: 125
• Page End: 135
• Publication Date: 2017-10
• Subjects: Back-propagation neural network -- Gray level co-occurrence matrix -- Laws texture energy measures -- Image processing; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2017.09.007
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Machine vision based inspection systems 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 feed-forward 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 feed-forward 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 feed-forward back propagation neural network. Mean Square 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.