# The Algorithms of Image Processing and Analysis in the Textile Fabrics Abrasion Assessment
## 3 Materials and Methods  
In order to specify the optimal image analysis algorithms for abrasion resistance assessment, the six fabrics--dedicated for different purposes and characterized by dissimilar structures--were investigated. The subjects of the tests were three woven fabrics dedicated for garments, two technical woven textiles and one upholstery textile product. The details of each fabric subjected to tests are presented in Table 1. Parallel with standard method assessments, the abrasion resistance of fabrics using an instrumental method was investigated. The images of certain samples, after each number of rubs, were captured in order to carry out dual evaluation of samples. The processing and analysis of grabbed images was carried out. Figure 1 presents the scheme of the acquisition stand, Figures 2 and 3 show the set of stages and processes that were necessary for the whole experiment and instrumental method as well. There were two steps of the sample image analysis. Firstly, the preliminary analysis for selecting effective algorithms was carried out. Secondly, the final analysis, involving whole range of captured image samples, using a chosen algorithm, was done. The images of abraded samples (mounted on a Martindale device sample holder) were taken using the stand, presented in Figure 1. The stand  
Figure 1: The laboratory stand for image acquisition. consisted of a flat surface table with a fixed arm (1), dedicated for precise image acquisition, digital camera Canon EOS Mark II with prime 50 mm lens (2) and the Crimi-Lite 80L (3) lamp, which is a linear, 100 klux intensity light source (3a). The stand is located in room light conditions, because of the high intensity of the lamp, the machine vision scenes are almost steady. The images of abraded samples were captured in steady-state light conditions. During the preliminary analysis, image processing and analysis techniques were involved, for example, cutting of edges for processing images, conversion from RGB colour space to CIELAB, resulting tree particular channels-L-general brightness level, *a-brightness level for green and red components of colour, *b-brightness level for blue and yellow ones. The CIELAB standard is based on the perception model of the human eye, because it seems to be closest to the visual sensations of observer. The following algorithms, described in publication [21] and known as potentially sensitive, were chosen for the investigation: based on image histogram calculation (skewness, kurtosis) and global transformation (FHT-2D Fast Hartley Transform, Gabor and wavelet transforms). Both preliminary and final image processing analysis were carried out using Image 1.52 (Fiji) software, equipped with essential plugins, which enabled the use of the following image analysis algorithms: the calculation of Fourier-related transform (algorithm 2D Fast Hartley Transform (FHT), represented as 32-bit float FHT attached to the 8-bit image that displays the power spectrum; high and low-pass filtering using wavelet transform (with the Haar wavelet, Mexican-hat wavelet and B-spline function as a scaling functions, by decomposition level 2); transformations based on Gabor filtering; and calculations of parameters based on a brightness level histogram in a CIELab colour space (mean brightness, median, skewness, kurtosis). Firstly, the algorithms were chosen taking into consideration previous research experience in this field (sensitivity to surface changes caused by the abrasion process) and the knowledge that the algorithms work. Secondly, the efficiency of each tested algorithm was evaluated. The subjects of preliminary analysis were the selected image samples coming from the first stage of the abrasion process, the middle stage and the end of the process.