Automatic measurement and recognition of yarn snarls by digital image and signal processing methods

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.