Quantitative detection of wood surface defects by image segmentation method

REN Zhong-xin; BI Jian-hua; XIE Lin; ZHANG Jun-feng

doi:10.3788/YJYXS20142905.0785

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Quantitative detection of wood surface defects by image segmentation method

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- Quantitative detection of wood surface defects by image segmentation method
- Chinese Journal of Liquid Crystals and Displays Vol. 29, Issue 5, Pages: 785-792(2014)
- 作者机构：
  
  1. 东北林业大学土木工程学院,黑龙江哈尔滨,150040
  2. 东北林业大学信息与计算机工程学院,黑龙江哈尔滨,150040
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20142905.0785
  CLC： TP391.4
- Received：30 April 2014，
  
  Revised：13 May 2014，
  
  Published Online：02 June 2014，
  
  Published：05 October 2014
- 稿件说明：
移动端阅览
任重昕, 毕剑华, 谢琳, 仉俊峰. 采用图像分割方法进行木材表面缺陷的定量检测[J]. 液晶与显示, 2014,29(5): 785-792

REN Zhong-xin, BI Jian-hua, XIE Lin, ZHANG Jun-feng. Quantitative detection of wood surface defects by image segmentation method[J]. , 2014,29(5): 785-792
任重昕, 毕剑华, 谢琳, 仉俊峰. 采用图像分割方法进行木材表面缺陷的定量检测[J]. 液晶与显示, 2014,29(5): 785-792 DOI： 10.3788/YJYXS20142905.0785.

REN Zhong-xin, BI Jian-hua, XIE Lin, ZHANG Jun-feng. Quantitative detection of wood surface defects by image segmentation method[J]. , 2014,29(5): 785-792 DOI： 10.3788/YJYXS20142905.0785.

摘要

随着木材加工业的集约化发展以及对木材表面加工质量高水平的苛求，传统的人工检测方式已经难以满足木材产品的加工生产。在了解木材表面缺陷的分类、缺陷产生原因和木材缺陷表面图像的特征的基础上，对比分析平均值法、最大值法和加权平均值法3种图像灰度化方法效果，并选定加权平均值法对木材缺陷图像进行灰度化预处理。在Matlab 6.5 GUI编程框架下实现木材缺陷检测系统，通过选取Isodata聚类迭代法、Otsu最大方差法、最大熵法和Sobel边缘分割法为基础的4种阈值化图像分割方法对木材缺陷特征的分割效果和分割速率进行实验对比分析。实验结果表明，运用Isodata聚类迭代法的图像分割方法能够快速准确分割图像实现木材缺陷检测。

Abstract

With the development of wood industry and the large requirement for the wood surface processing quality

traditional artificial detections have been difficult for satisfying wood processing production requirement. By knowing the classification of wood surface defects

defect causes and characteristics of wood surface defect images

we compare and analyze the average method

maximum method and the weighted average method on image gray effect

and the weighted average method is selected. In Matlab 6.5 GUI programming framework

we implement wood defect detection system by choosing the Isodata clustering iteration method

Otsu method of maximum variance

maximum entropy method and Sobel edge segmentation method for image segmentation and comparisons. We can achieve the fast and accurate image segmentation and wood material defect detection by using the Isodata clustering iteration method.

关键词

Keywords

references

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