This method extracts multiple independent fiber targets from the microscopic image and calculates their fineness. The method is as follows: First, take the fiber slice image from the field of view of the biological microscope of the CMOS or CCD image acquisition device; then separate multiple independent fiber targets from the image background that may contain bubbles or impurities. This process is widely used. A combination of differential filtering, median filtering and other filters reduces the impact 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 existing technology, the invention can avoid the influence of different collection equipment and lighting environment on the segmentation algorithm, and improve the stability of the fiber fineness measurement process and the accuracy of the measurement result. Fiber fineness is the most important parameter for evaluating quality. Traditional methods include manual inspection, airflow method, microprojection method and other methods summarized in the production process. Among them, refer to the International Standard IS0137-85 'Wool Fiber Diameter Test Method Projection Microscope Method' (GB 10685-89) and refer to the United States AATCC-20A-1995 'Quantitative Analysis Method of Linen and Cotton Blended Products Fiber Projection Method' (FZ /T 30003-2000) are the two main measurement standards. Both of these standards use a microscope projector to measure the fibers (more than 100) on each slide under 500 times magnification. Both the microscope method and the projector method have the problems of high labor intensity and low efficiency. The measurement operation of a sample has to concentrate on performing hundreds of thousands of alignment/counting operations under the microscope, which is a monotonous large-scale operation. Repetitive work can easily cause eye fatigue, and the resulting inefficiency and human error are inevitable. In addition, with the development of the textile industry
, the issue of inspection standardization, unified inspection procedures and unified measurement standards have been brought about. Finally, more and more measurement work needs to be completed on site in the workshop, which also puts forward requirements for the stability of the recognition algorithm under changing lighting conditions, which cannot be met by traditional methods. For this reason, the fineness measurement technology based on computer image recognition algorithms has attracted more and more attention. So far, there have been some software and related researches aimed at automatic fiber measurement. From a large number of literature searches, research and trials, it is found that most of these systems and studies focus on measurement under laboratory conditions. The algorithm mainly uses fixed thresholds, histogram thresholds or entropy-based segmentation methods to process grayscale images, and then The mathematical morphology method is used for segmentation and boundary extraction. A typical product commonly used in the industry is the 0FDA of Uster, Switzerland, which collects fiber images under a stroboscopic light source and transmits them to the system to complete automatic measurement. Some other special image processing methods, including Hilditc boundary thinning 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 pretreatment process of almost all methods is limited by the characteristics of the tested sample and the lighting environment, which makes the software system need additional equipment support in actual practical applications, which is not conducive to the realization of portable and industrial field applications. Therefore, the accuracy, adaptability and stability of fiber automatic measurement need to be improved. Aiming at the deficiencies of the existing fiber identification and fiber fineness measurement technology, the present invention provides a fiber fineness measurement method based on microscopic images, which can avoid the influence of different collection equipment and lighting environment on the segmentation algorithm, and improve the fiber fineness measurement. The stability of the process and the accuracy of the measurement results. In order to achieve the above objective, the idea of u200bu200bthe present invention is: the present invention is a significant improvement in the automatic measurement of fiber fineness in microscopic images. The image can be derived from a CCD or CMOS image capture device and is processed and used by a set of filters The constrained i^ast Marching automatic identification algorithm makes the fiber identification, positioning process and fineness calculation result to a certain extent not affected by changes in the lighting environment.
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