The Application And Research Of Digital Image Processing On Wood Surface Texture Inspection

The wood surface defect detection is an interdisciplinary technology, which has a higher value in the field of timber production and deep processing. In this paper, burrow,dead knot,slipknot are the most three common wood defects for the study, conducted in-depth research on the wood surface defect pattern recognition method. The main contents include wood surface defects image segmentation, feature extraction and defect type recognition problem and so on.Image segmentation is the most important issue of the wood defect recognition. This article introduces the traditional edge detection algorithm. For watershed over-segmentation, wood defect image segmentation based on morphological watershed segmentation method has been proposed. Improvements have been made for the deficiencies of edge effect based on the fractal parameters wood surface defects segmentation method, it has put forward increased dimensional matrix calculation method. For timber defects color features, clustering segmentation method based on color moments has been proposed combined with fuzzy C-means clustering algorithm. While using mathematical morphology tools with powerful computing capabilities, processing the segmented image to strengthen the visibility and the integrity of the divided image and improve the accuracy of the defect extraction.The extraction of the characteristic quantities directly affect the recognition rate of the timber defect detection system. The feature extraction of wood defects image segmentation using Tamura texture,GLCM,wavelet multi-resolution fractal dimension were selected for classification with BP neural network and support vector machine classifier, Which takes multi-resolution fractal dimension as the input of BP neural network classifier, regardless of the training function, the classification accuracy rate reaches92.67%. In support vector machine classifier, Tamura texture and gray symbiotic matrix combined10parameters recognition accuracy rate is up to96.67%. The recognition accuracy rate of Multi-resolution fractal dimension is up to94%, which is higher than the BP neural network classifier.The test results show that,(1)support vector machines works in wood defect image classification, in particular, it demonstrates many unique advantages in solving small sample, nonlinear and high dimensional pattern recognition.(2) Using digital image processing technology, they are identification and effective ways to resolve issues to take segmentation based on the color characteristics of the wood surface defect images to solve the segmentation of the wood surface defects and based on defect image texture features.