Wood Board Defects Sorting Based on Method of Possibilistic C-Means Improved Support Vector Data Description

摘要

This paper exposes automatic classification for wooden board according to its knot defects. A four-parameter-classification vector, which includes the knot size and red component pixels of interior and exterior and boundary part of the knot, is formed to recognize the knot type. A possibilistic c-means (PCM) improved support vector data description (SVDD) method was proposed to construct a multi-classifier to classify four types of wood knots. The results obtained with our method show a real improvement of the recognition rate, which is 94%, compared to the original SVDD classifier, which recognition rate is just 86%, and experiments also testify PCM can help SVDD overcome the shortage of being sensitive to the noises and outliers.