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

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.

  Info
Periodical
Chapter
Chapter 5: Model and Optimization and Simulation
Edited by
Zhixiang Hou
Pages
1288-1291
DOI
10.4028/www.scientific.net/AMM.128-129.1288
Citation
Y. Z. Zhang, C. Tong, H. L. Yu, J. Cao, "Wood Board Defects Sorting Based on Method of Possibilistic C-Means Improved Support Vector Data Description", Applied Mechanics and Materials, Vols. 128-129, pp. 1288-1291, 2012
Online since
October 2011
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$32.00
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