A Method of Extracting Flotation Foam Motion Feature Based on r-k Algorithm

Abstract

The invention discloses a method for extracting motion features of flotation foam based on r-K algorithm. The idea of ​​the method is as follows: First, use an industrial camera to obtain a dynamic flotation foam image, use an accelerated robust characteristic operator to extract the feature points of the flotation foam image, and then combine the random consistency algorithm and the filtering algorithm to process the feature points of the flotation foam image. Matching, calculate the velocity and entropy of flotation foam movement on the basis of this method, and verify the feasibility and effectiveness of this method. The simulation results of industrial experiment data show that this method has low algorithm complexity, high matching rate of feature points, and robustness. strong, can effectively extract the dynamic characteristics of the flotation foam, the present invention is applicable to the gold-antimony flotation production process, can be used for the establishment of the identification model of the production working conditions in the gold-antimony flotation process, for reducing the misjudgment rate of working conditions, improving It is of great significance to improve the grade of gold-antimony concentrate, save energy, reduce the amount of chemicals added, reduce environmental pollution, and realize the optimization of gold-antimony flotation process conditions.

Description

technical field [0001] The invention relates to the fields of image processing technology and the like, in particular to a method for extracting motion features of flotation foam based on the r-K algorithm. Background technique [0002] Flotation is the most widely used mineral processing method, and its function is to separate useful minerals from ore. For a long time, the production operation of the dressing plant has relied on experienced workers to observe the flotation foam with naked eyes. There is no objective standard for judging the foam, which makes it difficult for the mineral flotation process based on manual observation to be in a stable and optimal operating state. . Using machine vision instead of human vision, using image processing technology to extract the most significant and effective visual features from the foam image, and objectively describing the flotation foam, can provide operational guidance for real-time control and optimization of the mineral f...

AI Technical Summary

Problems solved by technology

However, the state of the foam in flotation of different minerals may be completely different. In the flotation of gold and antimony, the movement characteristics of the flotation foam directly reflect the quality of the flotation performance. The movement characteristics of the flotation foam generally include the foam movement speed , foam stability, etc., but there are still many problems in the existing flotation foam motion feature extraction methods, such as low calculation accuracy, insufficient feature points, and can only calculate the overall motion trend, but not the internal motion trend, etc.

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