Quantitative analysis of blended textiles is important for manufacturers, retailers, and independent testing labs. In ours research, an automatic system for identifying ramie and cotton fibers in the longitudinal view was designed to substitute the manual examination, and a method that can automatically capture well-focused fiber images is proposed.So far, there have been many studies on fiber properties based on longitudinal fiber images, such as the evaluation of fineness and maturity of cottons [1], the identification of convolutions in cotton fibers [2], and the measurement of fiber length [3]. 1To take longitudinal measurements, fibers are usually cut into short segments and spread on a glass slide. A mass of the fiber segments is located at different positions on the sample slide. Up to now, sequential capturing has still served as a conventional fiber image capturing method [1].Abstract This paper introduces a new fiber imaging system that can automatically capture a series of microscopic fiber images in the longitudinal view to form a panoramic image of long-fiber segments for reliable identifications of blended fibers. The panoramic image shows thumbnails of long fibers without missing or duplicating any segments on the sample slide. Firstly, the sample slide is scanned quickly in the X-and Y-axes to capture sequential images of fibers and to register the locations of each image. Secondly, a panoramic image is formed by stitching the sub-images together according to their positions. Thirdly, the panoramic image is analyzed to calculate the skeletons of long-fiber segments, which provide simple representations of fiber shapes and locations, and to register the locations of individual segments for the second scan of the slide. Finally, the triaxial motorized stage transports the slide to the location of each registered segment, readjusts the focus, and captures high quality images for the formal analysis. The entire capturing process is fully automated, and the panoramic view permits efficient image capturing for the reliable identification of ramie and cotton mixed fibers.
Based on the analysis of stripes on fiber surfaces, a new method for cotton and ramie fiber identification is introduced in this paper. The stripes of a fiber surface were extracted by segmentation, edge detection, and thinning, and then they were orthogonally projected along the curving skeleton of the fiber. In addition, six characteristic parameters for identification were obtained, and based on the method of maximum probability, equations for identification were established on the six probability distribution curves of the characteristic parameters. Finally, weight coefficients of the equations were obtained from self-adapting identification tests. The experiments showed that the overall tolerance for false identification of cotton or ramie fiber was under 7%.
Fiber diameter and its distribution are the fundamental parameters affecting the performance of melt-blown nonwoven materials. This paper proposes a new method to measure diameters of microfiber in nonwoven based on image processing techniques. The one-pixel-wide boundaries of potential fibers were extracted first. The real fiber profiles were then separated from incorrect rectangles by a recognition procedure. Finally, the fiber diameters and diameter distribution were calculated. The experimental results show that the new method is consistent with the manual methods in measuring the main fiber diameter and fiber diameter distribution of melt-blown nonwoven material and has many advantages including efficiency, reproducibility, and objectivity.
Lyocell is a cellulosic fiber manufactured through a more environmentally friendly process. As lyocell and cotton have many complementary properties, such as bending elasticity, deliquescent effect and gliding property, they are often blended for use in dresses and formal wear for better comfort and drape. The quantitative analysis of lyocell and cotton blends is important for manufacturers, dealers, retailers, service providers or suppliers of blended textiles, and for independent testing labs.In our previous research, an automatic system for identifying ramie and cotton fibers was designed [1,2]. The system can automatically capture a series of microscopic fiber images in the longitudinal view to form a panoramic image of long-fiber segments, and then locate each valid frame to capture well-focused fiber images. Six characteristic parameters based on the Y-projection and X-projections of fiber stripes [1-4] were established for the fiber classification. These parameters, denoted as P i (i = 1~6), are listed in Table 1. The correlation analysis of the six parameters for ramie and cotton fibers revealed that they are linearly dependent [2]. 1 In this study, we will expand the function of this system for lyocell and cotton fiber identifications. Since the image capturing and feature extraction methods remain the same, we will focus on more effective pattern recognition Abstract This study applies cluster analysis to identifying two frequently blended fibers, lyocell and cotton, based on the six characteristic parameters extracted from the automatic fiber identification system presented in the previous publications. Two independent parameters are first derived from the six characteristic parameters by using factor analysis. Second, a probability density distribution map of the two indirect parameters is established through sample observations. Finally, the clusters of lyocell and cotton fibers in the probability density distribution map are segmented according to contour lines and distance. The experiment showed that the accuracy of lyocell and cotton fiber identification with the cluster analysis is above 95%. Table 1 Definitions of six fiber parameters.P 1 The average width of a fiber. P 2 The number of wave crests in the fluctuant fiber-width curve with values over 25% of the average fiber width in the range of one millimeter. P 3 The projective width of the X projection.P 4 The average of the subpoints on the Y projection.P 5 The number of the wave crests in the X projection.P 6 The depth of the deepest trough in the X projection.
It is difficult to capture a completely clear image of nonwoven web which is thicker than the depth of field of a light microscope. This phenomenon will leads to the data loss and the test error. In this paper, a region-based image fusion algorithm based on fibers natural boundaries was proposed. First, the one-pixel-wide boundaries were extracted from the point-based image fusion process. Then, the image fusion regions were formed by the sharpness diffusion from the source points which have the highest sharpness at local gradient within the boundaries of fibers. Finally, a fused clear image of the nonwoven web was constructed by replacing the regions with the corresponding regions which have the highest sharpness gradient from the series images at different focus positions of the light microscope.
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