Comprehensive Recognizing Method And Device Of Woven Fabric Constituent Elements Based On Image Process And Analysis

Woven fabric structure, including structure and quality, not only refers to woven fabric characteristics, but also presents fabric fundamental quality. People used to assess fabric appearance, physical (mechanical) quality according to the application characteristics, most of assessments are qualitative, which are inadequate for identifying fabric properties and application features. It is highly desirable to develop a new simple and quantitative approach to find the relationship of fabric structural elements and fabric properties, which is important to give advice to production and use.The constituent elements of fabrics include fabric texture, structure, yarn composition, fiber type, and blending ratio and so on. The best effective method of obtaining so much information from fabric is using the same sample on the same mechanics, but there are no such textile fabric measurement theory and devices combining multi-feature and easy interaction.In this paper, we present a novel approach by which the same samples are tested on the same mechanics, carrying out synchronization tests in a single operating system according to the basic evaluation of the constituent elements of fabrics.Research results are as following areas:(1) Develop a measuring device (FaCFAS system) integrating light, machines, electricity, computing and control technology. FaCFAS system consists of a zoom, high-resolution, dynamic camera system, multi-source optical system and mechanical measurement system components. It can been effectively used to capture clear image and obtain materials' mechanical properties and structural features, providing a new mean to measure the constituent elements of fabrics using digital image processing and analysis technology.(2) In order to overcome the difficulties and low precision for automatic recognition of yarn array and yarn density from woven fabrics such as tight structures, skew, twisted, suede or non-uniform structures, we invent a new mechanical Stretching & Correcting Technique. The sample is forced by biaxial stretching undestructively; it can not only improve the woven fabric structure, but also produce or enhance image features for yarn arrays. Normalized features can unify segmentation algorithm of yarns to achieve yarn location accuracy and yarn density measurements. The new method explored a new technology which combined stretching- correcting and image processing, and it is proved to be very effective and practical.(3) Analyze dyed yarn array:discuss mechanical stretching effect on the fabric color recognition, design a number of algorithms for yarn division and color analysis. Floating window arithme, which obtained dyed yarn information, can extract pure organizational point of the pixel; subtractive algorithm is designed to reduce the amount of data processing, simplify the computing process; improved Fuzzy C Means algorithm achieves dyed yarn separation; the designed palette algorithm, using Point-bit sampling and Color statistical value median as color, effectively overcomes effects of random color variation point of distortion, effects of which are better than mean-generated; a self-test function template composite algorithm corrects a few mistakes of dyed yarn arrangement diagram, the computational complexity is low, this algorithm are the initial recognition results of the second re-inspection and checking identification tools.(4) In the aspects of fabric recognition:propose the mechanical stretching and corresponding algorithm. According to the feature selection of genetic algorithm and the separable principle of feature integration, combination of characteristics of the data which can lead to variation of individual organizations is constructed to overcome the hairiness or uncertainties in order to reduce the characteristics of dimension and improve the performance characteristics at the same time. The role of mechanical stretching fabric reinforced the image texture features of the organizational point of the value of more heterogeneous separation, which reduces to light and dark features and texture features of the fabric analysis of two sub-modes. The smallest normalization and the organization point of the template matching algorithm and the algorithm in each composite examination are used for the organization of multiple parallel samples'pattern grid. In this way, a few of the original pattern grid misjudgment points can be corrected. The rate of reconstruction of pattern grid which is corrected can be up to 100%.(5) In the aspects of fabric yarn structure measurement:First, if the yarn diameter is measured from the basic point of the fabric, there is different measuring accuracy from different points. It is the projecting width of the yarn from the organizational point that is best in according with the actual diameter in the linearity. And the relative error of correcting is the minimum.So it should be selected as the measurement points. Each sample should be measured 26 times in accordance with the articles. The confidence level should be taken as 0.95. In this way, the error between the measured value and the true value is less than 3.5%.There is reference value for the prediction of some properties of yarns and fabrics by the calculation of pressing coefficient of the yarn which brought forward highly practical value among all the geometric parameters of yarns. If the fabric's is smaller, then it is softer in handle, lighter in texture, the contact area covered is larger, wind proofness is better. This kind of fabric is suitable for autumn and winter.Secondly, the image's Overall Specification is used in detecting texture's direction to transform the problem of overall detection which is rather difficult in spatial domain to transform domain problem which is suitable for the shape and texture detection.The inclination angle algorithm based on the Fourier transform of fiber is designed. The main direction of texture form is extracted from the angular power spectrum. The sample and impact of technical factors result in fluctuations in measurement results. And the representativeness and stability has not been satisfied.The Hough transform algorithm based on the fiber angle is designed. Even when the fiber is bonded into blocks, the probability of the straight line representing the direction of texture remains the largest.Texture's shape is expressed with simple data structure whose anti-noise capability and intuitive are better. The correlation coefficient with manual measurements is up to 0.959. The error under control keeps within 5%. It proves potential applications.Thirdly, in a long time, the yarn density (δ) which is seen as the expression of the mean has been in use on the basis of simplifying assumptions and formulas of the classical theory for a long time, which can not correctly reflect the fiber distribution in yarns. This paper is based on the classical model of relation between yarn diameter and yarn linear density. The variationδfollowing the variation of elastic yarn structure is used to lead to the contour changes in the yarn, while the twist angle can reflect the variation in the law of elastic yarn structure. This algorithm uses a supervised way. The twist angle's correction factor kβtoδis introduced, because yarn density used to be a constant quality concept is difficult to obtain their distribution through weighing due to the random change of quality along the length. So, image-based shape feature of indirect measurement is a new method to achieve yarn density (δT)distribution.