Ore extraction and analysis from RGB image and 3D Point Cloud

被引：0
|
作者：
Jin, Feng [1 ]
Zhan, Kai [1 ]
Chen, Shengjie [1 ]
Huang, Shu Wei [1 ]
Zhang, Yuansheng [1 ]
机构：
[1] BGRIMM Technol Grp, Beijing, Peoples R China
来源：
GOSPODARKA SUROWCAMI MINERALNYMI-MINERAL RESOURCES MANAGEMENT | 2022年 / 38卷 / 01期
关键词：
ore image; 3D point cloud; embedded confidence edge detection; mean-shift; cross-calibration; MEAN-SHIFT; SEGMENTATION;
D O I：
10.24425/gsm.2022.140612
中图分类号：
P57 [矿物学];
学科分类号：
070901 ;
摘要：
Based on the theory of computer vision, a new method for extracting ore from underground mines is proposed. This is based on a combination of ROB images collected by a color industrial camera and a point cloud generated by a 3D ToF camera. Firstly, the mean-shift algorithm combined with the embedded confidence edge detection algorithm is used to segment the ROB ore image into different regions. Secondly, the effective ore regions are classified into large pieces of ore and ore piles consisting of a number of small pieces of ore. The method applied in the classification process is to embed the confidence into the edge detection algorithm which calculates edge distribution around ore regions. Finally, the ROB camera and the 3D ToF camera are calibrated and the camera matrix transformation of the two cameras is obtained. Point cloud fragments are then extracted according to the cross-calibration result. The geometric properties of the ore point cloud are then analysed in the subsequent procedure.
引用
收藏
页码：89 / 105
页数：17
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