基于自适应谷底检测的浮选泡沫形态特征提取

Morphological Characteristics Extraction of Mineral Flotation Froth Based on Adaptive Valley Edge Detection

针对矿物浮选泡沫大小形状不一、光照不均等特点,提出一种基于适应度反馈微粒群优化算法自适应优选谷底检测阈值的泡沫形态特征提取方法。将微粒群算法的惯性权重和加速因子设置为全局最优点适应度的函数,优选谷底检测阈值,采用局部灰度极小选择边界检测模板,根据多角度逻辑规则比较,获取气泡边界,分割结果的统计分析表明泡沫形态特征服从gamm a分布。

Considering the d ifferent size and shape and poor illum ination ofm ineral flotation froth,the mor-phological characteristics extraction of m ineral flotation froth was proposed based on the adaptive valley edge detection of fitness feedback particles swarm optim ization（FFPSO）.Having the inertia weight and accelera-tion factors of PSO taken as the globalm inima function to choose best detection threshold,and having the local gray scale m inima based to select edge detection template,and as well as having the multi-angle logical rules compared to detect froth boundary.Statistical analysis of segmentation results shows that the froth morphologi-cal characteristics are gamma d istribution.