基于最大熵与密度聚类相融合的毛羽检测

为能够更加精确地计算出纱线毛羽的根数及毛羽长度,基于最大熵与密度聚类相融合对纱线毛羽的长度及根数进行检测。该方法首先利用双边滤波对采集到的纱线图像进行预处理,滤除图像中的噪声,同时增强纱线毛羽特征;然后利用最大熵对预处理后的纱线图像进行阈值分割,去除条干提取毛羽,并对毛羽进行细化;最后利用密度聚类算法(DBSCAN聚类)对细化后的毛羽进行分类统计,根据所分类的个数以及每类所含像素点的个数计算出毛羽的根数及长度。将实验结果与目测法和基准线法进行比较,结果表明,该方法与目测方法检测的结果非常接近,结果比基准线法更加精确,检测结果准确、有效。

Hairiness detection based on maximum entropy and density clustering

In order to calculate the number of yarn hairiness and hairiness length more accurately,a method based on maximum entropy and density clustering was proposed to detect the yarn hairiness length and root number. The yarn image was preprocessed by bilateral filtering to filter out the noise in the image and enhance the yarn hairiness characteristics. Then the maximum entropy was adopted to segment the preprocessed yarn image and remove the yarn. The hairiness were extracted and refined. Finally, the density clustering algorithm(DBSCAN clustering) was applied to classify the number of hairiness. In addition, the root and length of hairiness according to the number of classified hairiness and the number of pixels in each class were calculated. Compared with the visual method and the datum line method, the experimental results demonstrate that the proposed method is very close to the visual method and more accurate than the datum line method. Furthermore, it is shown that the proposed method is accurate and effective.