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Fiber fineness measurement method based on microscopic image1
The method extracts multiple independent fiber targets from a microscopic image and calculates their fineness. The method is as follows: first, the fiber slice image is taken from the field of view of the biological microscope of the CMOS or CCD image acquisition device; then multiple independent fiber targets are separated from the image background that may contain air bubbles or impurities. A combination of differential filtering, median filtering and other filters can reduce the influence of impurities and different lighting conditions; then use the Fast Marching algorithm to locate all fibers in the segmented image; finally, calculate the fiber fineness to complete the measurement of all fiber fineness . Compared with the prior art, the invention can avoid the influence of different collection devices and illumination environments on the segmentation algorithm, and improve the stability of the fiber fineness measurement process and the accuracy of the measurement results. The most important parameter for evaluating the quality of fiber fineness, traditional methods include manual inspection, airflow method, microprojection method and other methods summarized in the production process. Among them, the 'Wool Fiber Diameter Test Method Projection Microscopy Method' (GB 10685-89) formulated with reference to the international standard IS0137-85 and the 'Fiber projection method for quantitative analysis of hemp-cotton blended products' formulated with reference to the American AATCC-20A-1995 (FZ /T 30003-2000) are the two main measurement standards. Fibers (over 100 fibers) per slide were measured using a microprojector at 500X magnification in both standards. Both the microscope method and the projector method have the problems of high labor intensity and low efficiency. The measurement operation of a sample requires hundreds of thousands of alignment/counting operations under the microscope. Repeated work can easily cause eye fatigue, and the resulting inefficiencies and human errors are inevitable. In addition, with the development of the testing-equipment' target='_blank'>textile industry, the problem of inspection standardization, unified inspection procedures and unified measurement standards has been brought about. Finally, more and more measurement work needs to be done on-site in the workshop, which also puts forward requirements on the stability of the recognition algorithm under changing lighting conditions, which cannot be satisfied by traditional methods. For this reason, the fineness measurement technology based on computer image recognition algorithm has attracted more and more attention. To date, several software and related studies have emerged for automated fiber measurement. From a large number of literature searches, investigations and trials, it is found that most of these systems and studies focus on measurements under laboratory conditions, and algorithms mainly use fixed thresholds, histogram thresholds or entropy-based segmentation methods to process grayscale images, and then Mathematical morphology is used for segmentation and boundary extraction. A typical product commonly used in the industry is the OFDA of Uster, Switzerland, which collects fiber images under a stroboscopic light source and transmits it to the system to complete automatic measurement. Some other special image processing methods, including the Hilditc boundary refinement method, or the method of using neural network recognition based on feature extraction, have also been proposed one after another. However, the practical application of these methods is not yet mature, and most methods require manual assistance in the measurement process. In addition, the preprocessing process of almost all methods is limited by the characteristics of the tested samples and the lighting environment, which makes the software system require additional equipment support in actual practice, which is not conducive to the realization of portable and industrial field applications. Therefore, the accuracy, adaptability and stability of automatic fiber measurement need to be improved. Aiming at the deficiencies of the existing fiber identification and fiber fineness measurement technologies, the invention provides a fiber fineness measurement method based on microscopic images, which can avoid the influence of different collection devices and illumination environments on the segmentation algorithm, and improve the fiber fineness measurement. Process stability and measurement accuracy. In order to achieve the above objects, the concept of the present invention is: the present invention is a significant improvement in the automatic measurement of fiber fineness in microscopic images. Constrained i^ast Marching automatic identification algorithm makes fiber identification, positioning process and fineness calculation results unaffected by changes in lighting environment to a certain extent. Compared with the existing corresponding technology, the technology improves the recognition stability under variable illumination environment, is suitable for CCD and CMOS image acquisition equipment, optimizes the speed of algorithm implementation, and meets the requirements of accuracy. According to the above-mentioned inventive concept, the present invention adopts the following technical solutions:—A fiber fineness measurement method based on microscopic images, which is characterized in that a plurality of independent fiber targets are extracted from the microscopic images with air bubbles or impurities, and the fineness measurement for all fibers is completed; the specific measurement process includes the following
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textile testing equipment also offers several other tensile tester manufacturers that could potentially be useful for manufacturers.
Ball Burst Method (ASTM D3787): Steel ball penetration for textiles/films using testers like GT-C02-2.
Hydraulic Method (ISO 13938.1): Fluid pressure on rubber diaphragm for industrial fabrics via GT-C12A.
Pneumatic Method (ISO 13938.2): Compressed air for breathable materials tested with GT-C12B.
Results are influenced by raw materials, yarn properties,
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