@ARTICLE{Jin_Feng_Image_2021,
 author={Jin, Feng and Zhan, Kai and Chen, Shengjie and Huang, Shuwei and Zhang, Yuansheng},
 volume={vol. 37},
 number={No 4},
 journal={Gospodarka Surowcami Mineralnymi - Mineral Resources Management},
 pages={133-152},
 howpublished={online},
 year={2021},
 publisher={Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN},
 publisher={Instytut Gospodarki Surowcami Mineralnymi i Energią PAN},
 abstract={In the execution of edge detection algorithms and clustering algorithms to segment image containing ore and soil, ore images with very similar textural features cannot be segmented effectively when the two algorithms are used alone. This paper proposes a novel image segmentation method based on the fusion of a confidence edge detection algorithm and a mean shift algorithm, which integrates image color, texture and spatial features. On the basis of the initial segmentation results obtained by the mean shift segmentation algorithm, the edge information of the image is extracted by using the edge detection algorithm based on the confidence degree, and the edge detection results are applied to the initial segmentation region results to optimize and merge the ore or pile belonging to the same region. The experimental results show that this method can successfully overcome the shortcomings of the respective algorithm and has a better segmentation results for the ore, which effectively solves the problem of over segmentation.},
 type={Article},
 title={Image segmentation method of mine pass soil and ore based on the fusion of the confidence edge detection algorithm and mean shift algorithm},
 URL={http://ochroma.man.poznan.pl/Content/122197/PDF/Jin-i-inni.pdf},
 doi={10.24425/gsm.2021.139742},
 keywords={edge detection, confidence, mean shift algorithm, image segmentation},
}