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INTRODUCTION The quality of cotton fiber depends on a large set of characteristics which includes length, maturity, fineness, strength, colour, trash. Considerable improvements have been made in these measurements. Methods used both in high volume instruments and in low volume apparatus make possible measuring a great number of fibers. The data col...
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... we treat a two dimensional longitudinal cotton image analysis. Five specimens were examined in this paper from selected raw cotton. H.V.I. principal characteristics of cotton specimens are given in Table I : Samples were cleaned and paralleled by combing. Then, cut into 1 mm snippets. After, the snippets were transferred into a microscope slide and covered with a cover glass. The images were captured at a 512x512 spatial resolution by a CCD camera which was mounted on a Zeiss microscope. The purpose of image pre-processing is to reduce the noise in the background. To be done, firstly, image enhancement was used to improve its quality. Also, an averaging filter was used to smooth the image before binarization. Secondly, the grey scale image was converted to a binary image by an automatic thresholding technique. Then, the cotton fiber image was shown white on a black background. At next, erosion and dilatation operations were applied to clean background noise, eliminate small holes present inside the fibers and fill gaps in the contours. Ultimately, connected components analysis was applied to the binary image to delete the remaining objects treated as noise by using a size filtering. Note that, after this last step, we have enlarged each image by adding zeros matrices to its four borders. This was done to keep fibers away from image borders. Figure 2 and Figure 3 show examples of captured images of cotton fibers obtained before and after pre-processing treatment. Also, we present an example of images which contains two connected fibers to explain the different steps of our processing procedure. The image processing procedure permit respectively the localisation of the medial axis of segmented fibers, the individualisation of connected fibers by analysing junctions, the identification of fiber segments and the separation of the edges of the two sides of each fiber segment. At the first step, a thinning technique was applied to the segmented images to obtain the medial axis of fibers. This technique removes edge pixels iteratively so a fiber without holes shrinks into one-pixel-thick line segment. It has the same principle as skeletonization. But, as shown in Figure 4 and Figure 5 , it has the advantage that it returns individual fibers with no artefacts and medial axis without any ...
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The quality of cotton fiber depends on a large set of characteristics which includes length, maturity, fineness, strength, colour, trash. Considerable improvements have been made in these measurements. Methods used both in high volume instruments and in low volume apparatus make possible measuring a great number of fibers. The data collected is nec...
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... The direct methods are based on microscopic evaluations of some geometric parameters in cross sectional or longitudinal views. 27 reported three of the most significant direct methods include: 1-The caustic soda swelling test, for determining three parameters of cotton fiber maturity coefficient of maturity: percentage of mature fiber and maturity ratio. 28,29,30 stated that the maturity obtained after swelling the fibers in alkali may not represent the true botanical maturity, also 31 have demonstrated that swelling technique will grade the varieties maturity in an order different from what exists before treating with NaoH. ...
... that all of these parameters are better measures for fiber maturity. These results are in line with 27,48,50 . Simple correlation regression coefficients between measured and calculated degree of thickening of the Egyptian cotton genotypes illustrated in Table 4 and Figure 1 were positive and highly significant. ...
The present study was carried out in Cotton Technology Res. Department Cotton Res. Institute, Agric. Res. Center at Giza in 2017 season. Aiming to conduct a comparative study on fiber maturity measurements of the Egyptian cotton using direct and indirect methods. Lint cotton samples of three micronair levels (Mic levels) from six Egyptian cotton genotypes representing ELS, Delta and Upper Egypt LS genotypes were tested by HVI, cutter & causticare and Cross section-Image analysis mothds. The results indicated that mature fibers have bigger hair weight (HW), Perimeter (P), area of cross section (ACW), area of secondary wall(ASCW) and degree of thickening(Ɵ) than low mature fibers; HVI MR was nearly similar to caustic soda MR. HW1, HW2 and HW3 ranked the studied genotypes in the same order. Calculated ASCW and degree of thickening values using1.52 cellulose density was closer to what obtained from Image analysis direct reference method than those calculated using 1.14 cell wall density. Correlation coefficients between cotton fiber maturity and between calculated and measured degree of thickening were highly significant. Regression equations were developed to make corrections to the degree of thickening calculated from Lord equation, Hequet equation, cutter and causticsoda methods, HVI MR and caustic soda MR.
... The micronaire indicator (Mic) appears on the monitor. The micronaire index can be used to assess the thickness and maturity of cotton fibers [18][19][20]. If the micronaire index is less than 3.0 the fiber is considered too thin. ...
This article defines fiber quality indicators that differ in laboratory conditions from the upper, middle and lower layers of Bukhara-6 breeding varieties of cotton, in the modern system HVI 1000 SA. Based on the results of the study, histograms of changes in the quality of cotton fiber in the layers of the harem are presented. As an alternative, the quality indicators of yarn obtained in the laboratory spinning device “Sherli” of small size from fiber were determined. As can be seen from the analysis of the test results, it was found that the comparative elongation strength of cotton obtained from the lower layer of the stack, the upper average length, elongation at break, the light output coefficient, decreased compared to other layers of the stack, on the contrary, the index of hip fibers, increased, decreased compared to other layers of the stack. In addition, according to the results of the tests obtained, the fiber viscosity index was obtained – the correlation between the properties of fibers and the properties of yarn, the thread viscosity index was estimated by the CSP (COUNT STRENGTH PRODUKT) coefficient, which was determined by the formula for carded yarn obtained in the laboratory spinning device “Sherli” from fiber stored in the refrigerator. The obtained results showed that the relationship between fiber properties and yarn properties due to the fact that the maturity index of a thread is at the top of the stack compared to the middle and lower part of the stack.
... There are various methods adopted to measure fibre maturity such as image analysis technique [9], caustic air method [10] differential dyeing test [11], Uster AFIS [12], infrared spectroscopy [13], X-ray diffraction [14], among others. Moreover, some previous researches are aimed at the calculation of fibre maturity and its association with fibre fineness using both mathematic and statistical techniques. ...
Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.
... A number of papers have been published in recent years about computer vision algorithms for fibre studies. The reported studies include methods for industrial fibres [9,10], textile fibres [11][12][13][14] and paper fibres [15][16][17]. ...
... Ikiz et al. [11] described a method of measu ring the length of cotton fibres. Adel et al. [13] presented a method for defining goodness measures such as maturity index, circularity index, and fineness for cotton, as well as a simple strategy for distinguishing minimally overlapping fibres. Wang [12] discussed an algorithm with which the length and maturation index of cotton fibres is measured. ...
... This method involves a strategy to classify some of overlapping fibres to intra-fibre and inter-fibre cross-overs by utilizing the form of the intersection. In all of the above-mentioned papers, the fibre samples are trapped between glass slides [11,13] or a scanner [12]. In maturation index calculation, fibres are cut to smaller segments [12,13]. ...
An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 μm. Also, the choices made by the algorithm are much more consistent than the human choices.
... Computer image analysis system provided by Leica Company was used for the morphological investigation of the fibers before and after treatment. [22] RESULTS AND DISCUSSION ...
... Since the 1960s, people began to study machine vision and have acquired a series of achievements. Now machine vision is widely used in printing [3], electronics and semiconductor [4], medical [5], textile [6,7], agriculture [8] and other fields for its non-contact, high efficiency, stability, and real-time benefits. Especially in some dangerous work environments, machine vision inspection instead of manual can not only improve efficiency and automation, but also ensure the safety of staff. ...
An appearance defect online inspection system of mesh fabric has been developed based on machine vision. The mean filter method is adopted to eliminate noise. An adaptive threshold method based on brightness is presented to eliminate the effects of uneven illumination and separate the foreground and background. By analyzing the texture characteristics and defect features of mesh fabric, the mesh fabric defect identification method based on texture features is proposed. The results have shown when the online inspection speed reaches 60m/min, the defect recognition rate can reach 95% or more and the online inspection system can meet the automation requirements of enterprises quite well.
... Since the 1960s, people began to study machine vision and have acquired a series of achievements. Now machine vision is widely used in printing [3], electronics and semiconductor [4], medical [5], textile [6,7], agriculture [8] and other fields for its non-contact, high efficiency, stability, and real-time benefits. Especially in some dangerous work environments, machine vision inspection instead of manual can not only improve efficiency and automation, but also ensure the safety of staff. ...
An appearance defect online inspection system of mesh fabric has been developed based on machine vision. The mean filter method is adopted to eliminate noise. An adaptive threshold method based on brightness is presented to eliminate the effects of uneven illumination and separate the foreground and background. By analyzing the texture characteristics and defect features of mesh fabric, the mesh fabric defect identification method based on texture features is proposed. The results have shown when the online inspection speed reaches 60m/min, the defect recognition rate can reach 95% or more and the online inspection system can meet the automation requirements of enterprises quite well.
In the study, the effects of different model characteristics of garments on cutting and sewing times have been investigated. Following the experiment, a method for determining cutting and sewing times for sample product groups has been created. Product groups subject to the study are skirt and men's coat. In the study, 8 different models to the chosen skirt group and 7 different models to the men's coat group were applied. Total number of pieces, perimeter of pieces, cutting time, and sewing time were investigated. Pearson correlation was used in the study. Regression analysis was conducted to investigate the effects of other happenings on the observed process. Following the investigations of all factors and their degrees of effect, it was observed that a characteristic of a model is one of the important factors that affect the productivity and time in apparel company. The paper provides that different model characteristics of garments is very important for cutting and sewing times.
An auto-generation method of color image was researched based on the fast Fourier transform theory. We developed a program to auto-generate abundant exquisite color images. Samples of images, using patterns of points, were designed. The auto-generation color processing can be divided into the four steps: giving the image size, drawing basic pattern, giving the color pattern and transforming color image. The simplest basic patterns and their combinations were analyzed. HSV color model was adapted in the research. Three important parameters, including H, S and V, were considered in this research for image design. The results showed that the images have the properties of alliance quartet. The size and color of image element can be controlled according to requirement of designer. In the present research the images were transformed to textiles by virtual furniture software. The results also showed that the images can be used for textile designer directly. The application of the images was also discussed for the elements for textile image design such as furniture textile.
