Method for extracting Sobel operator filtering profile for representing fabric texture and fractal detail mixed characteristic vector

Abstract

The invention relates to a method for extracting a Sobel operator filtering profile for representing fabric texture and a fractal detail mixed characteristic vector, which comprises the following steps of: firstly, taking a group of gray scale statistics with respective unified calculation mode as profile characteristics on the basis of carrying out horizontal and vertical Sobel operator filtering on an original fabric image respectively; calculating the fractal dimension of each child window comprising a lateral basic cycle period or longitudinal basic cycle period in the original image based on the traversal method principle; selecting two fractal dimension extreme limits for reflecting lateral detail information and two fractal dimension extreme limits for reflecting longitudinal detail information as detail characteristics for representing the fabric texture; and combing the two Sobel operator filtering profile characteristics and four fractal detail characteristics to form a mixed characteristic vector. The mixed characteristic vector has high complementarity among all characteristics, combines the profile information and the detail information of the texture, also combines the lateral information and the longitudinal information of the texture and can describe the characteristics of the fabric texture comprehensively in detail.

Description

technical field [0001] The invention belongs to the field of digital image processing and pattern recognition, in particular to a method for extracting feature vectors mixed with Sobel operator filtering overview and fractal details for characterizing fabric texture. Background technique [0002] With the help of fabric texture characterization technology, the purposes of fabric texture parameter estimation, texture classification, fabric appearance evaluation, and defect detection can be realized. Any fabric texture contains two important information, namely general information and detail information. The overview information provides the overall rough structure and grayscale impression for human eyes or machine vision, while the detail information provides the local fine structure and grayscale impression. Therefore, in order to fully and meticulously characterize the texture structure and reflect the texture characteristics to the maximum extent, both the general appeara...

AI Technical Summary

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

[0014] The fabric texture characterization methods involved in the above-mentioned existing literature or patents are limited to the extraction of global features for the characterization of fabric texture information, and fail to take into account both the general appearance and detailed information of fabric texture, so they cannot comprehensively and meticulously characterize the essential characteristics of fabric texture

In addition, the main feature of the above-mentioned Sobel operator texture representation method is that after the texture image is filtered by the Sobel operator, a certain threshold must be selected to realize the binarization of the image

This has two main disadvantages: one is that it is difficult to select the optimal threshold for different textures; the other is that after the image is binarized, a large amount of grayscale transition information is lost, leaving only black and full White binary information, while the texture image to be processed usually has 256 gray levels

Therefore, the above processing method is cumbersome and the features extracted on this basis cannot achieve a more adequate and appropriate representation of the texture.

However, the above-mentioned documents or patents have the following disadvantages in the fractal feature representation of fabric texture: 1. The feature is directly extracted on the basis of a two-dimensional image, which requires a large amount of calculation; 2. The extracted fractal feature can only describe the global information of the texture, and cannot The detailed information of the fabric texture is meticulously and deeply represented; 3. The inherent warp and weft orientation characteristics of the fabric texture are not fully utilized in feature extraction to improve the stability of the feature; 4. The feature extraction does not fully utilize the inherent regular cycle characteristics of the fabric warp and weft yarns. Improve feature accuracy and stability

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