Multiple features extraction for timber defects detection and classification using SVM

Timber defects detection is one of the important topics in machine vision applications, since the number and severity of defects determine the quality of the wood and consequently its price. In this paper we propose a method to detect wood defects such as cracks and knots. Firstly we create a dictionary based on the bag-of-words approach in a training step. The dictionary is obtained either using LBP and SURF features alone or with a combination of both features. In the second step an image processing pipeline which associates contrast enhancement, entropy maximization and image filtering is used to detect the potential defect regions and we proposed to use SVM classifier to detect knots and cracks. The proposed algorithm is evaluated on two different datasets which have knots and cracks as groundtruth. The experimental results show that our method achieves a precision of 0.92 and 0.91, and a recall of 0.94 and 0.96 for the Epicea and Pine datasets respectively with multiple features based dictionary.