应用灰度直方图特征识别木材表面节子缺陷

Wood Knot Defects Recognition with Gray-Scale Histogram Features

木材表面节子是木材缺陷中非常重要的一类缺陷,也是评定木材外观等级、锯材和单板质量的重要指标。为了提高节子缺陷识别效率及准确性,并改善检测过程的自动化程度,对应用木材表面图像的灰度直方图统计特征进行节子缺陷识别进行研究。通过利用类间距离对7个统计特征的分类能力进行评价,从而确定出识别节子缺陷的最佳统计特征,即平滑度特征;同时提出一种自适应的最大类间方差聚类法进行分类阈值的确定,进而采用阈值判别实现节子缺陷识别。经在线检测实验证实,该方法的识别率高于99%。

The knot on the wood surface is a very important kind of wood defects, and it is the key specification for assessing the appearance grade and the quality of lumber and veneer. To enhance the accuracy and efficiency of knot defects recognition, and improve the automatic level of detecting procedure, the recognition of knot defects by using the statistics features of gray-scale histogram from wood surface image is studied. The classifying ability of seven statistics features is evaluated through using the between-cluster distance, and hence the optimal statistics feature that recognizes the knot defect is determined, such as the smoothness. At the same time, an adaptive clustering method with maximal between-cluster variance is presented to determine the classifying threshold, and then based on that the knot defect is recognized. The online detection experiment shows that the recognition rate of the presented method is up to 99%.