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电磁磨矿分级系统中用于高效矿石颗粒检测的分割与边缘检测相结合的方法

Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System.

作者信息

Budzan Sebastian, Buchczik Dariusz, Pawełczyk Marek, Tůma Jiří

机构信息

Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Department of Control Systems and Instrumentation, VŠB-Technical University of Ostrava, 17. listopadu 15/2172, 708 33 Ostrava-Poruba, Czech Republic.

出版信息

Sensors (Basel). 2019 Apr 15;19(8):1805. doi: 10.3390/s19081805.

DOI:10.3390/s19081805
PMID:30991763
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6515149/
Abstract

This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack's theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features.

摘要

本文提出了一种用于铜矿石颗粒检测与分类的机器视觉方法。我们提出了一种将种子区域生长分割和边缘检测相结合的新方法，其中区域生长仅限于晶界。首先，计算二维快速傅里叶变换（2DFFT）和灰度共生矩阵（GLCM），通过剔除质量较差的样本提高检测结果和处理时间。接下来，基于区域生长进行铜矿石颗粒检测，并结合一阶和二阶导数、改进的尼布莱克理论以及阈值选择方法对其进行改进。最后，所有检测到的颗粒通过一组形状特征进行表征，这些特征用于将颗粒分类为不同的粒度级分。该算法的效率通过已知粒度的真实铜矿石样本进行评估。所提出的方法能够同时利用多个颗粒形状特征生成关于不同粒度级分的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c515/6515149/692d33f52e21/sensors-19-01805-g015.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c515/6515149/1034cb271f43/sensors-19-01805-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c515/6515149/1b153eaffe0c/sensors-19-01805-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c515/6515149/86587a20a17a/sensors-19-01805-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c515/6515149/692d33f52e21/sensors-19-01805-g015.jpg

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Estimates of edge detection filters in human vision.人类视觉中边缘检测滤波器的估计。
Vision Res. 2018 Dec;153:30-36. doi: 10.1016/j.visres.2018.09.007. Epub 2018 Oct 10.
3
Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform.基于局部自适应Canny边缘检测和改进的圆形霍夫变换的图像分析自动检测粒度分布
Micron. 2018 Mar;106:34-41. doi: 10.1016/j.micron.2017.12.002. Epub 2017 Dec 21.
4
Segmentation of retinal vessels by means of directional response vector similarity and region growing.基于方向响应向量相似性和区域生长的视网膜血管分割
Comput Biol Med. 2015 Nov 1;66:209-21. doi: 10.1016/j.compbiomed.2015.09.008. Epub 2015 Sep 21.