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Automatic Recognition Of Fabric Structures Based On Digital Image Decomposition

机译:基于数字图像分解的织物结构自动识别

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A method to recognize fabric structures automatically based on digital image decomposition has been introduced. The method includes establishing a Wiener filter adapted to the fabric texture. A woven fabric image can be decomposed into horizontal and vertical subimages by using this Wiener filter. These two subimages contain the weft and warp texture information respectively. After thresholding, the gray-level subimages are transformed into binary images, in which the weft or warp floats range periodically. Then the weaving density can be figured out. Based on the preceding work, the positional information of yarns in every single subimage can directly help to enclose every interlacing point. Considering the variety of gray value in each point unit, warp point and weft point can be distinguished. The basic structures for woven fabric (plain, twill and satin) have been evaluated and it is found that the density for woven fabric can be calculated exactly and the structures can be identified clearly.
机译:介绍了一种基于数字图像分解自动识别织物结构的方法。该方法包括建立适合于织物质地的维纳过滤器。使用此维纳滤镜,可以将机织图像分解为水平和垂直子图像。这两个子图像分别包含纬纱和经纱纹理信息。在阈值化之后,将灰度子图像转换为二进制图像,其中纬线或经线的浮动范围是周期性的。然后可以计算出编织密度。根据之前的工作,每个子图像中纱线的位置信息都可以直接帮助封闭每个交织点。考虑到每个点单位的灰度值的变化,可以区分经点和纬点。对机织物的基本结构(平纹,斜纹和缎纹)进行了评估,发现机织物的密度可以精确计算,并且可以清楚地识别出结构。

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