Extraction method of filtration detail characteristics of Sobel operator for presenting fabric grain

Abstract

The invention belongs to the fields of digital image processing and pattern recognition, and particularly relates to an extraction method of the filtration detail characteristics of a Sobel operator for presenting fabric grain. The method comprises the following steps of: firstly, respectively solving transversal and longitudinal basic cycle period span of a fabric image by adopting one-dimensional fast Fourier transformation; then, respectively carrying out horizontal and vertical Sobel operator filtration treatment on the fabric image; and calculating four extreme gray statistics as the detail characteristics on the basis according to the basic cycle period span of the fabric grain and a traversal method principle. Accordingly, the four extracted characteristics respectively present the transversal edge maximum statistical information, the transversal edge minimum statistical information, longitudinal edge maximum statistical information and longitudinal edge minimum statistical information of the grain so that the characteristic vectors formed by the four characteristics can fully present the details of the fabric grain and have mutual obvious complementarity. The invention avoids implementing binary processing to the image after the Sobel operator filtration and keeps richer usable information of the filtered image.

Description

technical field [0001] The invention belongs to the field of digital image processing and pattern recognition, in particular to a Sobel operator filtering detail feature extraction method for characterizing fabric texture. Background technique [0002] Sobel operator is one of the operators in image processing, mainly used for edge detection. Technically, it is a discrete difference operator, which is used to approximate the gradient of the image brightness function. Using this operator at any point in the image will generate a corresponding gradient vector. There are two Sobel operators, one to detect horizontal edges and the other to detect vertical edges. The Sobel operator uses two 3×3 direction templates or convolution kernels to perform neighborhood convolution with each point in the image in the image space to complete edge detection. One of the two direction templates is enhanced by approximating the vertical direction gradient. The horizontal edge of the image, a...

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Problems solved by technology

The report did not consider the basic cycle length of the texture period as the basis for feature extraction, did not explain the selection method of the binarization threshold, and the single feature extracted only involved the number of border point pixels, and did not clearly define the meaning of the border points The distribution of boundary points in the image

[0006] The main disadvantage of the Sobel texture characterization method involved in the above-mentioned existing literature is that the texture characterization is insufficient, the extracted features are single, mainly reflect the global information of the texture, and lack a deep and detailed description of the texture details

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