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

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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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