Image texture feature extraction method based on nested triangular structure

Abstract

The invention discloses an image texture feature extraction method based on a nested triangular structure, relating to a brand new gray different triangle matrix (GDTM). By extracting the gray difference of the nested triangle vertexes of the image at different positions and angles, counting the frequency of the gray difference, and performing normalization calculation to obtain a triangle matrix. The GDTM can reflect the distribution features of the brightness change, can reflect the comprehensive information of the image in different directions and different interval brightness changes, and is the image brightness change second-order statistic characteristic based on a triangle geometry structure. The texture characteristic quantity calculated on the basis of the invention can visually and effectively represent the image texture condition, compared with the conventional texture statistic feature extraction method such as the grey level cooccurrence matrix, the image texture feature extraction method provided is less in calculated quantity, is resistant to illumination change and RST change, can improve the description effect of large textures, and is an effective image texture feature extraction method.

Description

technical field [0001] The invention relates to an image texture feature extraction method, in particular to an image texture feature extraction method based on a nested triangle structure. Background technique [0002] Texture is the macroscopic manifestation of a certain characteristic local repeating pattern in an image. For most texture images, this repeating pattern is approximate and complex. Compared with other methods, texture analysis can make full use of image information and better take into account the image. It is an important technical means of image analysis and retrieval, computer vision, etc., and is widely used in military, medical, meteorological, information security, industrial production and testing and other fields. However, the types of textures are complex, the shapes are different, and the structures are complicated. The extraction and analysis of texture image features has always been a major problem in the field of image processing. Research on mo...

AI Technical Summary

Problems solved by technology

For example, GLCM can represent the gray-scale pair probability statistics information of all pairs of pixels with different step lengths and different directions in the entire image, but in practical applications, researchers often choose one or a few combinations of step sizes and directions. The texture image information obtained is not complete, and further research based on this feature has missing image information, which is flawed.

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