基于深度图像分析的细粒度矿石分级测定方法

doi:10.3969/j.issn.0255-8297.2019.04.006

Abstract

Abstract: To improve the precision of image processing technology in fine-grained ore measurement, a grading determination method based on depth image analysis is proposed. On the basis of gray-level co-occurrence matrix (GLCM), a maximum linear dispersion method is proposed to generate the step size and gray-level compression level adaptively, and support vector machine (SVM) classifiers are optimized through grid search and crossvalidation thus to improve the accuracy of particle size measurement. Experimental results showed that this method can achieve an accuracy rate of more than 92% for fine-grained ores with particle sizes of 0~0.9 mm, 0.9~3.0 mm, 3.0~5.0 mm and 5.0~7.0 mm, which can fully meet the requirements of grading and determining fine-grained ores.

Key words: ore grain size, texture extraction, image classification, gray-level co-occurrence matrix, maximum linear dispersion

CLC Number:

TP391.4

LU Caiwu, QI Fan, RUAN Shunling. Grade Determination of Fine Grain Ore Based on Depth Image Analysis[J]. Journal of Applied Sciences, 2019, 37(4): 490-500.

References

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Grade Determination of Fine Grain Ore Based on Depth Image Analysis

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