基于FCM-WA联合算法的多种类矿石图像分割

doi:10.11872/j.issn.1005-2518.2023.01.148

Abstract

Abstract:

Ore image segmentation is an important part of ore size distribution detection based on machine vision.In order to solve the problem that it is difficult to recognize and segment the multi kinds of ore images with various colors，complex textures and adhesive edges in composite mines，a method of ore image segmentation based on FCM-WA combined algorithm was proposed.Firstly，the ore image is optimized by morphology，which uses bilateral filtering，histogram equalization and morphological reconstruction to optimize the geometric features of the ore image，reduce the impact of noise on the segmentation effect，and improve the image contrast.Then，the FCM algorithm was combined with the watershed algorithm，and the FCM algorithm was used for clustering iteration to calculate the appropriate segmentation threshold，segment the ore image，and output the binary image.Then，the WA algorithm based on distance transformation was used to optimize the segmentation result of FCM algorithm，and the edge conglutination part of ore image output by FCM algorithm was segmented to obtain the best segmentation image.The results show that：（1）Using morphological optimization process to process ore images can reduce noise，enhance edge information and improve contrast.（2） Compared with the traditional Otsu method and genetic algorithm，the FCM-WA method in this paper is more robust and has better segmentation effect.The accuracy of pixel segmentation and ore particle size recognition for multiple kinds of ore images can reach more than 92%.（3） The experiment results show that the FCM-WA method can accurately segment many kinds of ore images with diverse colors，edge adhesion and complex texture features，and the segmentation results meet the requirements of particle size distribution detection.（4） The FCM-WA method in this paper is in line with the production needs of real mining enterprises，and can provide reliable technical support for the development of new mine intelligent particle size detection equipment.

Key words: compound mines, ore image, morphological treatment, fast and robust fuzzy c-means clustering algorithm, watershed algorithm, edge segmentation

CLC Number:

TP391

Wencong TANG,Xiaoyan LUO. Image Segmentation of Multi Kinds of Ores Based on FCM-WA Joint Algorithm[J].Gold Science and Technology, 2023, 31(1): 153-162.

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算法	像素数 /个	正确分割像素数/个	TPR /%	矿石个数	正确识别个数	TOR /%
OTSU	2 320 470	1 930 961	83.21	268	96	35.8
GA	2 320 470	2 224 518	95.86	268	130	48.5
FCM	2 320 470	2 245 424	96.76	268	107	39.9
FCM-WA	2 320 470	2 198 471	94.74	268	262	97.7

图像	像素分割准确率（TPR）				矿石粒度识别准确率（TOR）
图像	OTSU	GA	FCM	FCM-WA	OTSU	GA	FCM	FCM-WA
1	83.2	95.8	96.7	94.7	35.8	48.5	39.9	97.7
2	89.8	97.3	98.6	96.2	38.4	45.5	38.4	92.4
3	84.9	95.0	97.1	94.5	34.3	37.7	33.4	96.1
4	88.4	96.4	97.7	95.1	39.2	48.2	44.8	94.3
5	91.4	97.7	98.9	96.5	44.3	45.2	42.1	95.9

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Image Segmentation of Multi Kinds of Ores Based on FCM-WA Joint Algorithm

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