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Automated knot detection with visual post-processing of Douglas-fir veneer images

机译:道格拉斯杉木单板图像的可视化后处理自动进行结检测

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Knots on digital images of 51 full veneer sheets, obtained from nine peeler blocks crosscut from two 35-foot (10.7m) long logs and one 18-foot (5.5m) log from a single Douglas-fir tree, were detected using a two-phase algorithm. The algorithm was developed using one image, the Development Sheet, refined on five other images, the Training Sheets, and then applied to all remaining sheets. In phase one, global thresholding was used to segment the image through a series of morphological operations to isolate regions likely to contain knots. In phase two, adaptive thresholding was applied to grey scale and red component segmented images to improve the accuracy of the segmented knot. Overall performance, judged in terms of confusion matrix performance metrics, was better for the red component images. Red component recall (true positive) rate was 1.00, 0.99, and 0.96 for the Development, Training, and complete sets, respectively. For the grey scale images, recall rates were 0.96 for all sets. Red component accuracy was 0.76, 0.92, 0.73 (Development, Training, and complete) and those for the grey scale images were 0.71, 0.85, and 0.69, respectively. Red component precision also exceeded that of the grey scale (0.75, 0.93, 0.73 compared to 0.72, 0.88, 0.70). A greater percentage of knots (78%) segmented from red component images were correctly sized, while 16% had more pixels than required and 6% had fewer pixels. Comparative figures for the grey scale images were 57% correctly sized, 2% with more pixels, and 42% with less pixels. Based on our results, we will adopt the red component image for continuing work with digital veneer images from a sample of Douglas-fir trees selected on the basis of acoustic velocity measures. Together with acoustic measurements of the veneer sheets, we are investigating the extent that the number, size, and spatial arrangement of knots influences the average stiffness of veneer sheets, with a view to determining if a relationship exists between the average stiffness of veneer sheets in a peeler block, stiffness of the log, and stiffness of the parent tree from a range of silvicultural treatments.
机译:使用两个相算法。该算法使用一张图像(开发表)进行开发,并在其他五张图像(培训表)上进行优化,然后应用于所有剩余的表。在第一阶段中,使用全局阈值处理通过一系列形态学操作对图像进行分割,以分离可能包含结的区域。在第二阶段,将自适应阈值应用于灰度和红色分量分割图像,以提高分割结的准确性。根据混淆矩阵性能指标判断的整体性能对于红色分量图像更好。开发,训练和成套工具的红色成分召回率(真实阳性)分别为1.00、0.99和0.96。对于灰度图像,所有组的召回率均为0.96。红色分量的准确度分别为0.76、0.92、0.73(开发,训练和完全),灰度图像的准确度分别为0.71、0.85和0.69。红色分量的精度也超过了灰度级(0.75、0.93、0.73,而0.72、0.88、0.70)。从红色成分图像中分割出的结节比例更高(78%)的大小正确,而16%的像素比所需的像素多,而6%的像素较少。灰度图像的比较数字正确大小为57%,像素更多为2%,像素更少为42%。基于我们的结果,我们将采用红色成分图像与数字单板图像继续工作,这些数字单板图像来自于根据声速测度选择的道格拉斯杉树样本中。结合单板的声学测量,我们正在研究结的数量,大小和空间排列对单板平均刚度的影响程度,以期确定单板的平均刚度之间是否存在关系。一系列造林处理的削皮器块,原木的硬度和母树的硬度。

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