Wood Surface Quality Detection and Classification Using Gray Level and Texture Features

  • Deqing WangEmail author
  • Zengwu Liu
  • Fengyu Cong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


Computer vision methods can benefit wood processing industry. We propose a method to detect wood surface quality and classify wood samples into sound and defective classes. Gray level histogram statistical features and gray level co-occurrence matrix (GLCM) texture features are extracted from wood surface images and combined for classification. A half circle template is proposed to generate GLCM, avoiding calculating distances at each pixel every time and speeding up the algorithm greatly. The proposed approach uses more pixel information than traditional four-angle method, resulting in a significantly higher classification accuracy. Moreover the running time demonstrates our algorithm is efficient and suitable for real-time applications.


Wood Surface Detection Texture Image Classification Gray Level Histogram Statistics Gray Level Co-occurrence Matrix 


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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.Department of Biomedical Engineering, Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina
  2. 2.Dalian Scientific Test and Control Technology InstituteDalianChina

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