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Scientia Silvae Sinicae ›› 2018, Vol. 54 ›› Issue (11): 111-120.doi: 10.11707/j.1001-7488.20181116

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Research on Defect Extraction of Particleboard Surface Images Based on Gray Level Co-Occurrence Matrix and Hierarchical Clustering

Guo Hui1, Wang Xiao1, Liu Chuanze2, Zhou Yucheng1,2   

  1. 1. Research Institute of Wood Industry, CAF Beijing 100091;
    2. School College of Information and Electrical Engineering, Shandong Jianzhu University Jinan 250101
  • Received:2018-04-02 Revised:2018-05-14 Online:2018-11-25 Published:2018-12-04

Abstract: [Objective] A method for extracting defect regions on image of particleboards surfaces using gray level co-occurrence matrix and hierarchical clustering was proposed in this paper,which separated the targets from images according to the texture differences between defects and normal parts,in order to solve the problem of inaccurate defect segmentation caused by particle board surface texture in board surface defect detection system.[Method] The surface image was divided into several small windows and the texture of each window was characterized by the statistical textural feature parameters of the gray level co-occurrence matrix. Then, a hierarchical clustering method was used to distinguish the defect windows and the normal windows using the texture feature parameters. Firstly,the values of the four structural factors were chosen to build the gray level co-occurrence matrixes for each window,including the window size,gray level,direction and step. Secondly, the classification ability and correlation of the 14 statistical parametersof textural features for gray level co-occurrence matrix were evaluated using fisher criteria and linear correlation. Bydoing this,the features which have better classification ability and low correlation were chosen. The feature vector of each window was composed of the selected features values and all of the feature vectors constituted a sample set. The sample set was clustered by the BIRCH hierarchical clustering algorithm. In order to obtain an accurate clustering result,an optimization strategy was proposed in this paper. The histogram of sum average was drawn and the number of wave peaks was counted as the target category quantity. When the number of classes generated by the initial clustering was larger than the target category quantity,the clusters having closer distance were merged, which can avoid the over segmentation caused by high clustering precision. Finally,according to the clustering result,the windows in the original image were all marked and the defect areas were extracted.[Result] In the experiment,particleboards with five types of defects including sundries,oil stains,glue spots,big wood shavings and loose regions were used,and the size of the surface images was 512 pixels×512 pixels. Defect regions were extracted by the method proposed in this paper. The result showed that the method can extract the defect area with an accuracy of 92.2% and a recall rate of 91.8%.[Conclusion] The surface defect extraction method based on gray level co-occurrence matrix and hierarchical clustering can be used to solve the problem of inaccurate defect extraction caused by the texture of the particleboard surface,and it provides a good support for the measurement and identification of the defectsinmachine vision board defect detection system.

Key words: gray level co-occurrence matrix, texture feature, hierarchical clustering, BIRCH algorithm, defect detection

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