Automatic Measurement and Recognition of Yarn Snarls by Digital Image and Signal Processing Methods
Automatic Measurement and Recognition of Yarn Snarls by Digital Image and Signal Processing Methods
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DOI：
10.1177/0040517508090483
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发表时间：
2008-05
影响因子：
2.3
通讯作者：
Bingang Xu;C. Murrells;Xiaoming Tao
中科院分区：
文献类型：
--
作者：
Bingang Xu;C. Murrells;Xiaoming Tao
本文提出了一种计算机化的方法，用于从水浴中捕获的纱线缠结样品图像自动测量和识别纱线湿缠结。经过图像采集、图像转换和单个扭结样本提取，从分离后的二值图像中提取纱线轮廓函数。将纱线轮廓函数作为一维信号处理，将快速傅立叶变换和自适应正交投影分解结合到纱线扭结特征的模式识别算法中。除了纱线缠结圈数外，该方法还可以准确有效地检测纱线缠结的高度和宽度，这是退捻法无法获得的。通过数值模拟，考察了纱线直径分布、缠结分布、随机噪声水平等因素对纱线轮廓函数的影响。
In this paper, a computerized method has been proposed for automatic measurement and recognition of yarn wet snarls from an image of snarled yarn samples captured in a water bath. After image acquisition, image conversion and individual snarled sample extraction, the yarn profile function was extracted from the separated binary image. Fast Fourier Transform and Adaptive Orientated Orthogonal Projective Decomposition were then incorporated into a pattern recognition algorithm of yarn snarl features by treating the yarn profile function as a one-dimensional signal. In addition to the number of yarn snarl turns, the method was also accurate and efficient for the detection of yarn snarl height and width, which are unobtainable by the untwisting method. The effects of various factors on the yarn profile function were numerically examined, including distributions of yarn diameter and snarl, and the level of random noise.