Composite texture feature extraction method for flotation froth image

Abstract

The present invention discloses a composite texture feature extraction method for a flotation froth image. The method comprises: firstly, in a grayscale quantization matrix of a froth image, acquiring a face neighborhood set of all central pixel points; then, for all the central pixel points, constructing a three-dimensional data table and obtaining a nested grayscale frequency table; again, acquiring an improved neighborhood grayscale correlation matrix; and finally, obtaining a new composite texture feature, wherein the feature integrates a size, a texture and a roughness degree of froth, and has relatively high stability and separability in reflecting a texture of flotation froth; and according to the extracted composite texture feature, it is easy to distinguish flotation froth images with different operating conditions in different ore grades, thereby having a relatively high accuracy rate of recognizing operating conditions. The composite texture feature extraction method for the flotation froth image provided by the present invention is simple and effective, and is very important to guide the recognition of froth operating conditions in a mineral flotation site.

Description

technical field [0001] The invention belongs to the technical fields of image processing, pattern recognition and mineral flotation, in particular to a compound texture feature extraction method of flotation foam images. Background technique [0002] The apparent characteristics of foam are a comprehensive reflection of mineral flotation conditions, and are considered to be closely related to the flotation effect. How to accurately extract the foam appearance characteristics closely related to key production indicators in the flotation process is the key to realize the identification of flotation conditions. For a long time, experienced workers on site have adjusted the working conditions by observing the state of the foam surface. Due to artificial arbitrariness and subjectivity, the flotation state is unstable and it is difficult to adjust to the optimal state, resulting in low utilization of mineral resources, resulting in Waste of resources. [0003] In recent years, t...

AI Technical Summary

Problems solved by technology

Since the traditional neighborhood gray level correlation matrix method only considers the number of the same or similar gray values ​​of the central pixel and its neighboring pixels, but does not consider the number of differences between them and the size of the difference, therefore, lost A large number of spatial distribution attributes that can reflect the pixel difference of the image are difficult to fully and accurately reflect the texture information of the foam surface

In addition, under different ore grade conditions, the froth image will be quite different, and the texture feature extraction method of the traditional neighborhood gray correlation matrix without considering the influence of ore grade is difficult to adapt to the accurate identification of froth flotation conditions needs

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