Evaluation of yarn characteristics using computer vision and image processing

Irregularity, hairiness and twist are among the most important characteristics that define yarn quality. This thesis describes computer vision and image processing techniques developed to evaluate these characteristics. The optical and electronic aspects such as the illumination, lens parameters and aberrations play crucial role on the quality of yam images and on the overall performance of image processing. The depth of field limitation being the most important restraint in yam imaging as well as image distortion in line scan cameras arising from digitisation and yam movement are modelled mathematically and verified through experiments both for front-lit and back-lit illuminations. Various light sources and arrangements are tested and relative advantages and disadvantages are discussed based on the image quality. Known problems in defining the hair-core boundaries and determining the total hairiness from yam images are addressed and image enhancement and processing algorithms developed to overcome these problems are explained. A method to simulate various yam scanning resolution conditions is described. Using this method, the minimum scanning resolution limits to measure the hairiness and irregularity are investigated. [Continues.

Hair density distribution profile to evaluate yarn hairiness and its application to fabric simulations

This paper describes a new method for yarn hairiness evaluation that calculates the hair densities at varying distances from the yarn core using an image processing technique. The method is based on integrating the number of pixels for distances incremented by a pixel size from the yarn core edge. The experiments with various yarns showed that the hair density distribution profile (HDDP) exhibits two different exponential behaviors one below and the other above approximately 0.75 mm from the core. A total hairiness index (THw) is also calculated using the total number of hair pixels. A good correlation is observed between the proposed THw and Uster’s H index especially for grey cotton and cotton/polyester samples. A novel technique that generates realistic yarn simulations using the total hairiness and the hairiness distribution data along with the diametric irregularity is introduced. The simulations created are compared with actual yarn images in a qualitative manner. A simple single jersey knitted fabric simulation algorithm is also described utilizing the yarn simulation including hair distribution data, which gives a more realistic simulated fabric appearance

Digital image processing and illumination techniques for yarn characterization

This paper describes various illumination and image pro- cessing techniques for yarn characterization. Darkfield and back-lit illuminations are compared in terms of depth of field tolerance and image quality. Experiments show that back-lit illumination is superior in terms of depth of field tolerance and contrast. Three different back-lit illumination configurations are studied: one simply employ- ing a light source placed behind the yarn, the other incorporating a field lens to increase the light intensity passing through the aperture, and the third using a mirror placed at 45° to the optical axis to enable imaging of two orthogonal views of the yarn core. Problems in defining the hair–core boundaries in high resolution yarn pictures are addressed and a filtering process is introduced for back-lit im- ages. A comparison of the diameter and diameter coefficient of variation percentage measurements for different illumination and im- age processing techniques is given for several yarn samples. The data are also correlated with Premier 7000 diametric irregularity tester and Uster Tester 3 irregularity measurements. © 2005 SPIE and IS&T

Simulation of photosensor-based hairiness measurement using digital image analysis

The hair-counting technique using photosensors is a common method to measure the hairiness of the yarns. However, the literature recognizes some deficiencies of the technique regarding the sensor limitations. This paper describes a computer vision approach to simulate the photosensors and to investigate the parameters effecting the hairiness measurement when using these sensors. An algorithm developed to simulate the photosensor signals is explained. The effects of sensor resolution, signal threshold level and selection of zero reference positions from the core are investigated. The correlation between the measurements taken from two different sides of the yarn core is also examined. Twenty yarn samples are tested using a Zweigle G565 hairiness tester, and the results are compared with the hairiness measurements from the simulated photosensor system using digital images

Yarn twist measurement using digital imaging

Digital image analysis techniques in the spatial and frequency domains for twist measurement of yarns are described. A spatial technique is developed to extract the twist angle through the analysis of the yarn core image. Then, a Fourier transformation technique is applied to yarn images to measure the orientation of the fibre on the yarn surface. Finally, a hybrid method that incorporates frequency domain filtering prior to spatial analysis is proposed. The trials show that spatial analysis is a fast method and can successfully predict the twist in the yarn. Fourier transformation technique is quite sensitive to the protruding fibres obstructing the yarn surface, which may result in measurements having high variations. For yarns having little amount of hairs protruding from the core, the results agreed reasonably well with actual twist levels. Frequency domain filtering in conjunction with the spatial analysis of the yarn surface is found to be superior in terms of accuracy. The twist values calculated using the more reliable diameter measurements with back-lit images together with twist angles from the front-lit images are found to be more accurate when compared with the actual values