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A large number of parameters can be used for the description of fibers, yarns and textile fabrics. The hairiness, being an important factor for the haptical as well as the optical appearance of garments and other fabrics, can be measured by diverse testing methods based on different measuring principles, for some yarns leading to different results. The poster thus gives an overview of a new method for the evaluation of yarn hairiness, based on a statistical evaluation of microscopic images. These pictures are firstly transferred into 1-bit black-and-white images of the yarn. Afterwards, the so-called random-walking method is performed on these pictures. This means that, starting from a random point, steps in randomly chosen directions (up, down, left, right) are performed repeatedly, leading to a random path along the picture (for a detailed description with textile-related examples see). From the distances between starting and ending point for a large number of random-walking processes, the so-called Hurst exponent H can be calculated. For a completely filled black area without internal white areas (Brownian motion), the Hurst exponent is H = 0.5, while not completely filled areas result in H < 0.5. The Hurst exponent is especially reduced for fine hairs in which a reverse step is often more probable than a step in the same direction as in the last step. Thus, the number and the fineness of hairs on a yarn influence the Hurst exponent. The poster shows a selection of different yarns and compares results of the new random-walking approach with conventional test methods.