A method for automatically generating a tetragonal continuous pattern based on content features

Abstract

The invention provides a method for automatically generating a tetragonal continuous pattern based on content features. The method is characterized in that firstly, the noise of a fractal image is filtered by a smoothing filter algorithm, and grayscale is carried out; then the edge distribution histogram of gray-scale image is detected and extracted by Canny, and the probability distribution of four edge elements is calculated to judge whether the fractal image needs to be extracted or not, then the content of the fractal image is retrieved and the pattern position is determined by using the rectangular window rotation mechanism, and finally, the quadrangle continuous pattern is generated by calling horizontal and misalignment methods, so that the pattern generation method of the inventionautomatically extracts patterns and generates tetragonal continuous patterns through the main steps of filtering, edge feature retrieval, pattern content positioning, pattern extraction and stitching, and the like.

Description

technical field [0001] The invention belongs to the technical field of textile design, and in particular relates to a method for automatically generating square continuous patterns based on content features. Background technique [0002] With the development of the fashion design industry and the maturity of related technologies in the field of computer-aided design, a large number of artistic pictures can be quickly and efficiently generated to provide design inspiration for designers. In the non-linear picture generated by computer graphics, the distribution of pattern subjects is not fixed. In practical application, it needs pattern extraction and organizational layout to improve the design effect. [0003] The generation process of the square pattern can generally be divided into three steps: preprocessing, pattern extraction, and tissue layout. Through preprocessing, the image noise is reduced and the edge of the pattern in the image is enhanced, and then the edge infor...

AI Technical Summary

Problems solved by technology

In the process of image matching, it proposes a SIFT feature extraction algorithm based on the color histogram to solve the congenital deficiency of the traditional SIFT algorithm for color feature extraction: first calculate the position, scale and direction of the feature points according to the traditional SIFT feature extraction algorithm; then With the feature point as the center and the fixed field size as the window, the calculation color histogram is added, and the color histogram is used for the first matching to narrow the search range of the image; finally, the second matching is performed according to the operator of the traditional SIFT feature point, and Arrange the matched images in order of similarity from high to low; however, the feature extraction algorithm does not introduce the window into the positioning of the pattern to extract the pattern

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