Fusing multifocus images for yarn hairiness measurement

Abstract

Abstract. Yarn hairiness has been an important indication of yarn quality that affects weaving production and fabric appearance. In addition to many dedicated instruments, various image analysis systems have been adopted to measure yarn hairiness for potential values of high accuracy and low cost. However, there is a common problem in acquiring yarn images; that is, hairy fibers protruding beyond the depth of field of the imaging system cannot be fully focused. Fuzzy fibers in the image inevitably introduce errors to the hairiness data. This paper presents a project that attempts to solve the off-focus problem of hairy fibers by applying a new imaging scheme-multifocus image fusion. This new scheme uses compensatory information in sequential images taken at the same position but different depths to construct a new image whose pixels have the highest sharpness among the sequential images. The fused image possesses clearer fiber edges, permitting more complete fiber segmentation and tracing. In the experiments, we used six yarns of different fiber contents and spinning methods to compare the hairiness measurements from the fused images with those from unfused images and from the Uster tester.

A New Method to Evaluate Yarn Appearance Qualities Based on Machine Vision and Image Processing

“…Yarn hairiness refers to the number of fibers protruding outside the yarn core, which has a direct effect on yarn properties, weaving efficiency, and fabric appearance. As an important indicator, the index of yarn hairiness has been included in yarn quality requirements [11]. As shown in Figure 4(c), the yarn can be divided into the yarn core and the hairiness.…”

“…At present, there are four main methods for yarn diameter measurement. The first method is the test of capacitance evenness, in which the air capacitor is used as a sensing element [11]. When a piece of yarn is delivered between the two metal plates where an electric field exists, the capacitance will be changed with the amount of fiber.…”

“…With the fast development of computer science and high resolution image sensors, machine vision is widely used in various detection fields and plays a more and more critical role. Many literatures have reported the use of machine vision to detect yarn diameter [11], [18]- [20]. These methods are characterized by high reliability and efficiency [21], [22], and can resolve many complex problems which are resolved difficultly with the traditional methods [23], [24].…”

Multi-perspective measurement of yarn hairiness using mirrored images

“…We first applied the Otsu thresholding algorithm 15 to convert the original image to a binary image, and the morphological opening to remove thin fibers in the binary image. 1 The structuring elements selected for the opening were a disk-shaped template of 4 pixels in radius. We then executed the dilation operation in the image with a disk-shaped template of 2 pixels in radius.…”

“…Yarn hairiness, the number of protruding fibers outside the yarn core, has an adverse effect on the weaving or knitting production and on the appearance of end-use products. 1,2 The development of yarn hairiness measurement methods can be dated back to the 1950s. 3 Since then, various methods, including singeing, microscopy, photoelectric and image-processing techniques, have been adopted for hairiness measurement.…”