@ARTICLE{Jin_Feng_Ore_2022,
 author={Jin, Feng and Zhan, Kai and Chen, Shengjie and Huang, Shuwei and Zhang, Yuansheng},
 volume={vol. 38},
 number={No 1},
 journal={Gospodarka Surowcami Mineralnymi - Mineral Resources Management},
 pages={89-106},
 howpublished={online},
 year={2022},
 publisher={Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN},
 publisher={Instytut Gospodarki Surowcami Mineralnymi i Energią PAN},
 abstract={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 RGB 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 RGB 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 RGB 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.},
 type={Article},
 title={Ore extraction and analysis from RGB image and 3D Point Cloud},
 URL={http://www.czasopisma.pan.pl/Content/122693/PDF-MASTER/Jin%20i%20inni.pdf},
 doi={10.24425/gsm.2022.140612},
 keywords={ore image, 3D point cloud, embedded confidence edge detection, mean-shift, cross-calibration},
}