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

Abstract

The invention belongs to the field of digital image processing and mode recognition, in particular relates to a method for extracting a fractal profile for representing fabric texture and a Sobel operator filtering detail mixed characteristic vector, which comprises the following steps of: firstly, longitudinally and laterally projecting an original fabric image respectively and synchronously, combining two time sequences obtained by projection into one sequence, and estimating the fractal dimension of the sequence as a profile characteristic on the basis; subsequently, respectively carrying out lateral and vertical Sobel operator filtering on the original fabric image, and extracting four extreme value gray scale statistics as detail characteristics based on the basic cycle period of thefabric texture and the traversal method principle; and finally, combing the one fractal profile characteristic and the four Sobel operator filtering 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, and in particular relates to a method for extracting mixed feature vectors of fractal overview and Sobel operator filtering 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 gener...

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

[0013] 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 representation of fabric texture: 1. The fractal features are directly extracted on the basis of two-dimensional images, which requires a large amount of calculation. 2. The extracted fractal features can only describe the global information of the texture, and cannot be detailed Deeply characterize the detailed information of fabric texture 3. The inherent warp and weft orientation characteristics of fabric texture are not fully utilized in feature extraction to improve the stability of features

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