JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2010, Vol. 40 ›› Issue (5): 137-140.

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Wood defect recognition based on BIRCH cluster algorithm

WU Dong-yang, YE Ning   

  1. School of Information Technology, Nanjing Forestry University, Nanjing 210037, China
  • Received:2010-04-02 Online:2010-10-16 Published:2010-04-02

Abstract:

A new method for wood defect recognition based on BIRCH algorithm is been proposed. The problems about branch factor (B, L)、the selection of threshold T and the discrimination of non-defect class are been discussed. To produce the initial clustering, distinguish non-defect class for the initial clustering, automatically identify the location of the wood’s defects and mark it, a CFtree within a certain threshold is been built. The experimental results show that this algorithm can identify the wood’s defects efficiently, the average defecting precision ratio is about 86.3%, and the average defecting recall ratio is about 90.1%.
 

Key words: BIRCH, clustering method, wood defect

[1] XU Shan-shan, LIU Ying-an*, XU Sheng. Wood defects recognition based on the convolutional neural network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(2): 23-28.
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