Image analyzer and method for measuring linear fractal dimension of granular materials

Abstract

The invention provides an image analyzer and a method for measuring line fractal dimensions of a granular material, solving the problems that the conventional image analyzer can only obtain two-dimensional image data of the granular material, the line fractal dimensions of the granular material can be only obtained by using power exponents and fractal dimension relationship regression analysis after the two-dimensional image data are processed, and moreover, the regression analysis coefficient is relatively low and the fractal dimension result cannot be directly read. The technical key points are as follows: a data processing system comprises a measuring and control module, a granular image processing module and a line fractal dimensional calculation module, and a method is used in match with the image analyzer. According to the image analyzer and the method, by adding the granular image processing module and the line fractal dimensional calculation module in the measuring and control software of a granular image analysis system, a new granular two-dimensional image outline fractal dimension measurement function is added to the granular image analysis system on the basis of the conventional functions, and the measurement result of the two-dimensional image line fractal dimensions of the granular material can be directly output.

Description

technical field [0001] The invention relates to the technical field of granular material detection, in particular to an image analyzer for measuring the fractal dimension of the contour line of a two-dimensional image of the granular material and a method for measuring the line fractal dimension of the granular material. Background technique [0002] Fractals are shapes in which parts resemble the whole in some way. Fractal dimension (also known as fractal dimension) is playing an increasingly important role in characterizing irregular shapes and material properties that are difficult to characterize by traditional methods, such as agglomeration of particles, needle-like materials and sheet-like materials. A large number of experimental results show that: the two-dimensional image profile of granular materials satisfies statistical self-similarity and belongs to random fractal. [0003] At present, the methods for defining the fractal dimension of two-dimensional image cont...

AI Technical Summary

Problems solved by technology

What they have in common is: 1) The fractal satisfies a power law, and the relationship between the power exponent and the fractal dimension is a linear relationship, where the yardstick method is: , the box dimension method is: ; 2) The results of fractal dimensions of similar materials calculated by linear regression analysis fluctuate greatly; 3) The image analyzer can only obtain two-dimensional images of granular materials and other characterization results, and cannot calculate and analyze the contours of two-dimensional images to get the line fractal dimension

[0004] Existing image analyzers can only obtain two-dimensional image data of granular materials. After processing the two-dimensional image data, the linear fractal dimension of the granular material can only be obtained by applying the above-mentioned regression analysis of the relationship between the power exponent and the fractal dimension, and the regression Relatively low analysis coefficients, no direct reading of fractal dimension results

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