基于图像分析的堆浸铀矿石颗粒参数辨识

采用数字图像处理技术对铀矿石颗粒参数进行测量，并确定铀矿石块度分布.首次将图像引导滤波器应用于矿石图像滤波，较好地滤除了图像噪声和保持矿石边缘细节信息.采用基于最大类间后验交叉熵准则的PCNN图像分割算法分割矿石图像，减少了矿石粘连现象.为了解决第一次分割后矿石粘连现象，采用基于凹点匹配的数字图像切割算法对粘连的矿石图像进行第二次分割，能有效分离粘连矿石图像.采用基于形状特征的颗粒参数测量法测量颗粒参数，提高了颗粒参数的测量精度，得到了矿石块度的统计分布图.实验数据表明，该方法测量误差较小，能满足实际需求. 

Parameters Identification of Particle Size of Heap Leaching Uranium Ore Based on Image Analysis

Digital image processing technology was used to measure particle size of uranium ore fragmentation and determine particle size distribution.Image guided filter was applied to ore image filtering for the first time,which could filter image noise and preserve ore image edge information.PCNN image segmentation algorithm based on between-class posterior maximum cross entropy criterion was used to segment ore image,which could reduce ore adhesion phenomenon.In order to solve ore adhesion phenomenon after first image segmentation,a digital image cutting algorithm based on concave point matching was proposed to secondly segment adhesive ore image,which could effectively separate adhesion ore image.The parameter measurement method based on shape features was used to measure particle parameters,which could improve measurable accuracy of particle parameters.The statistical distribution charts of ore particle size were drawn with particle parameters.Experimental data show that the method can improve measure accuracy and satisfy with practical needs.