Image segmentation method for micro-fine cohesive core particles based on angular point and curvature detection

Abstract

The invention discloses an image segmentation method for micro-fine cohesive core particles based on angular point and curvature detection and is suitable for use in research on crushed mineral particle images. The method comprises the following steps: firstly, performing preprocessing on a mineral image; secondly, performing Harris angular point detection on an obtained binary image; thirdly, identifying concave points, that is to say, cohesive particle connecting points by utilizing curvature information of each angular point, adopting a certain rule according to characteristics of the concave points, determining an optimal segmentation path, and finishing segmentation of the cohesive ore particles. According to the method, the concave points are identified in combination with the angular point and curvature information by searching the angular points existent in a target region, the target region of the image is segmented through the directivity characteristics of the concave points and the nearest neighbor rule, and segmentation of the cohesive particles in the whole ore particle image is finally finished; the method is simple and can effectively segment a region of a large amount of cohesive particles in the image and restore distribution situation of the micro-fine ore particles in the image to the maximum extent.

Description

technical field [0001] The invention relates to an ore particle image segmentation method, and is especially suitable for a fine-grained ore particle image segmentation method based on corner point and curvature detection used in research on broken mineral particle images. Background technique [0002] The main purpose of mineral processing is to separate the useful minerals in the raw ore. One of the key steps is to grind the raw ore to achieve the purpose of dissociation of useful minerals. There is a certain relationship between the particle size of the grinding and the degree of dissociation. , so the accurate detection of ore particle size is an important technology. Generally, mineral technology uses the sieving method to detect the particle size. This method uses a limited number of sieves to measure the ore particle size, and the error is relatively large. At present, it is an accurate method to use image processing to segment and identify ore particle images. For ...

AI Technical Summary

Problems solved by technology

Generally, mineral technology uses the sieving method to detect the particle size. This method uses a limited number of sieves to measure the ore particle size, and the error is large.

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