Abstract: For the purpose of achieving automatic recognition and objective judgment of the production states of the froth phase,statistical modeling of froth images is introduced in the flotation process monitoring.Firstly,the Weibull distribution is applied to model the statistical distribution of the edge response of the froth images of all-round orientations,which leads to the effectively extraction of the spatial structure features of the froth images.Successively,a Mixture of Gaussian (MoG) model of the statistics of the froth images under each typical froth state is obtained by the statistical learning of the structure features of the froth image samples.Consequently,the froth states can be inferred effectively by the Bayesian inference.The froth state recognition results indicate that the proposed method can monitor the on-line spatial structural changes of the froth images,which achieves more accurate recognition results of the froth states comparing to the other froth image perception based froth state recognition.

Key words: mineral flotation, process systems, imaging, statistical modeling of images, measurement, Weibull distribution, operational status classification

摘要： 通过泡沫图像统计建模，实现了基于图像空间结构感知的浮选泡沫状态自动识别与客观评价。首先，采用Weibull分布建立了泡沫图像各方向边缘响应结构的统计分布模型，有效获取了泡沫图像空间结构的统计分布细节；然后，通过统计学习获得各典型工况状态下的泡沫图像边缘响应统计分布的混合高斯（MoG）模型；最后，通过简单的贝叶斯推理推断出测试泡沫图像对应的工况状态。结果表明：所提出的方法因有效获取了与浮选生产性能直接相关的泡沫空间结构的统计分布特征，可以实时监视泡沫空间结构的变化情况，泡沫生产状态识别准确率高。

关键词: 矿物浮选, 过程系统, 成像, 图像统计建模, 测量, Weibull分布, 工况分类