Woods Recognition System Based on Local Binary Pattern

Abstract

Malaysia is the largest exporter of tropical woods in the world, accounting for 70 percent of the world's supply of raw-logs. Sabah and Sarawak, the two Malaysian states on the island of Borneo, occupies some of the oldest and the most diverse rain forest in the world. Malaysia has a rich variety of tree species, and the wood produced from each of these has unique structure, physical and mechanical properties. The differences in woods structure and properties allow for the manufacture of woods based products with many different appearances and uses. In order to use this precious material efficiently, proper species must be used in the appropriate places. Intelligent Woods species recognition is a new application studied in the Computer Vision field to help prevent misclassifying of woods species in woods industries. Woods recognition is an implementation on identifying the different species of woods provided with the images captured for the woods samples or the characteristics observed. In this study, the features from the enhanced woods images are extracted using the LBP histogram, which determines the classification between the various woods species. The recognition is performed using a nearest neighbor classifier in the computed feature space with Chi square as a dissimilarity measure. The intelligent woods recognition system is designed to explore the possibility of developing a system which is able to perform automated woods recognition based on woods anatomy. The result thus obtained shows a high rate of recognition accuracy proving that the techniques due to its rotation invariance and robustness to gray-scale variations are very promising for practical applications.