Characterization Of Fiber Shape Factor And Automatic Recognition Of Profiled Fiber Based On Image Analysis Of Cross-sectional Shape

Fabrics made of profiled fibers with non-circular cross sections can be found on the functional wear market and widely used for sportswear. Changing the cross-sectional shape is the easiest way to alter the meachanical and aesthetic properties of a fiber. This is commonly done by changing the shape of the spinneret hole to produce the fiber shapes desired. Compared to the circular section fibers, fibers with modified cross sections possess more desirable qualities, such as increased luster, firm hand, bulkiness, soil resistance and reduced pilling. As for profiled fiber, quantitative characterization of fiber cross-sectional shape is an extremely important part of component analysis and quality control. It is indispensable when studying the influence mechanism of fiber cross-sectional non-circularity on the performance of end-use products.However, present methods of quantitative analysis on the cross sections of synthetic fibers are usually approached by eye observation and approximate measurement. The current standard test method of synthetic fiber shape factor has its shortcomings. For example, it is quite possible for fibers with different non-circularity degrees but identical measured shape factors. And the profiled fiber identification in cross-sectional view relies on subjective judgments, which is very time-consuming and tiring. Therefore, it is urgent to provide an automated system to complete cross-section shape analysis of profiled fiber.Based on this, the problem fallen into the scope of automatic image analysis system of profiled fiber cross-sectional shape in the condition of established automatic system of acquisiting microscopic images of fibers. An image processing system of profiled fiber cross-sectional photomicrographs is established, and then cross-sectional shape analysis of profiled fibers, quantitative characterization of shape factor and automatic identification of profiled fiber are investigated. We propose a new measure of shape factor which can be used to characterize all kinds of solid cross sections of profiled fibers. We propose an effective method of cross-sectional shape characterization for profiled fiber identification by comparing the extracted distance fluctuation curves of fiber cross-sectional boundary to the centroid. And we complete automatic identification of profiled fiber by means of a new sequential clustering algorithm based on dissimilarity measures among feature vectors.For image processing on profiled fiber photomicrographs, possible types of noises and their distribution are analyzed in this paper. The specificity of microscopic image processing is analyzed from the view of quantitative characterization of shape factor and profiled fiber identification. The pipeline of image processing which is suitable for profiled fiber photomicrographs are presented with two key steps which can greatly improved the accuracy of image segmentation. (1) Based on the characteristics of profiled fiber photomicrographs, an image enhancement technique of maximizing the contrast of the target (the boundaries of fibers) and the background (the texture of fiber cross-section and the body of resin) is proposed; (2) The accuracy of segmentation are greatly improved by computing the smallest convex polygon according to the registered objects. Experimental results show that the noises can be well controlled and the non-uniform illumination phenomenon can be eliminated effectively by the proposed image enhancement algorithm. The method of segmentation post-processing by calculating the smallest convex polygon can well protect the integrity of the boundary of fiber, while easily and accurately removing noises. The accuracy of image segmentation algorithm can be improved significantly. Compared with the common edge detection method, the proposed method is more effective because it can eliminate the double edge and false edge to extract the intact boundary with minimum noises. From the perspective of post-processing, this can reduce the difficulty of feature extraction.For quantitative characterization of shape factor, we present a new measure CVr2 and its modified measure CV(?)(a more reasonable weighted measure based on moments) of profiled fiber shape factor by extracting the distance-versus-angle function of the fiber cross section. Different from the traditional method that based on the inscribed circle and the circumscribed circle, our method is based on geometric moments. Furthermore, CV(?) can be applicable to any arbitrary shape with solid cross section, no matter whether it is convex or concave. Therefore, the values of CV(?) of different cross sections can be directly compared. Since CV(?)does not relevant with any measure of shape factor currently being used, it can provide a unified basis for quantification characterization of fiber shape factor.One of the advantages of CV(?)comes from its obvious translation, scaling, and rotation invariance property. It ranges over the interval [0,+∞) and CV(?)equals to the minimum 0 if and only if the cross- sectional shape is a perfect circle. The behavior of CV(?)is demonstrated on some regular shapes and practical cross-sections. Experimental results show that the defined measure can depict the discrepancy degree between fiber cross-section and the area equivalent circle. Compared withγ(γ=P2/4·π·A-1), CV(?)performs better in the case of cross-sectional shapes with deep concaves,γis sensitive to the change of the perimeter caused by the presence of deep concaves in cross-sectional shapes, whereas CV(?)does not have this disadvantage. Compared with CV,, CV(?)is more sensitive to the convex contours while less insensitive to the contour of deep concaves, more according with the real contacts among fibers. As an area-based measure, it is robust against noise.For automatic identification of profiled fiber in cross-sectional view, we propose an effective method of cross-sectional shape characterization for profiled fiber identification by comparing the extracted distance fluctuation curves of fibers'boundaries to the centroids and we also proposed a new sequential clustering algorithm based on dissimilarity measures among feature vectors.The distance fluctuation curve contains sufficient information on both the shape and the size of the cross section. Therefore, it can be used as the descriptor of cross-sections for profiled fiber identification. However, due to the randomly distributed fibers and shape deformation occurred in the microscopic images, it is hard to caliberate the starting point of fiber object in terms of image processing. In this paper, this challenge is tackled by finding the maximum value on the co-relationship curve, which intrinsically normalized the distance fluctuation curve. For two fiber cross-sections, the similarity degree of their normalized boundary fluctuation curves normalized can effectively reflect the similarity degree of themselves. Based on this, our method extracts the curves of all fiber cross-sections in one slide to compare the similarity degrees between each other, and then creates clusters to identify them. The proposed sequential clustering algorithm is based on dissimilarity measures among feature vectors. The well-know Euclidean distance are adopted as the proximity measure. The time complexity of this algorithm is O(N2).Although it is higher than that of the basic sequential algorithmic scheme (BSAS), for a given object, it can be clustered correctly if the distance between this object and any element of the class is lower than a given threshold. Such clustering result is independent on the order in which the vectors are presented to the algorithm. The experiment of verification of the clustering algorithm using real profiled fiber cross sections has also been done. The average recognition accuracy rate is about 97%. It is shown that the defined shape descriptor and the classification method can effectively identify solid profiled fibers that differ in their cross-sectional shapes. The distance fluctuation curve can characterize profiled fiber cross-sectional contour for profiled fiber identification effectively. The normalization method is also feasible. In addition, this curve is not sensitive to the calculation error caused by the discrete nature of digitial images. It is also insensitive to a certain degree of compressive deformation of cross sections during sample preparation.The resulting output of this system consists of two parts:TXT document and BMP image files. The document records the data of the result of shape factor calculation and fiber identification including area, aspect ratio, tag number, class number and component percentage. The image file is for intuitive output of fiber classification.After achieving the function of quantitative characterization of shape factor and profiled fiber recognition, batch testing is performed to validate the effectiveness, stability and reproducibility of the whole system through various shapes of profiled fiber. And the impact factors of the system are analyzed and discussed. Experimental results show that the shape factor calculation of our system is stable on 6 types of cross-sections and the recognition accuracy is stable too on 6 types of samples. The test on the system reproducibility has achieved good results. The output can be reproduced by using the same slide.In addition, further investigation on the extended application of our system has been made. Taking wickability of fabric as an example, the influence mechanism of fiber cross-sectional modification on the wicking properties of fiber assembles is analyzed. According to the experimental data existed in relevant literatures, we make a preliminary analysis and verification of the usefulness of the shape factor measure CV(?)proposed in this paper on textile industry. It shows that CV(?) has a predictive effect on the wicking property of fabric, and it can be used to analyze the effect of non-circularity degree of profiled fiber, especially for functional fiber with characteristic of moisture absorbing and drying quickly, which implies a potential valuable direction of using our proposed index.