Adaptive Image Processing for Bubbles in Flotation Process

In this paper, an adaptive segmentation based image processing method is presented for mineral flotation processes so as to obtain an optimal quality profile measure. An optimal structural element (SE) is firstly provided for initial image segmentation where fuzzy c-means (FCM) algorithm is combined with a watershed algorithm. Then, the extracted fuzzy texture spectrum feature is discriminated as either under-segmentation or over-segmentation regions. Finally, the segmentation results are subsequently processed to estimate the output probability density function (PDF) of bubble size distribution, which is the basis of the fault detection and real-time control for flotation processes. Experimental results demonstrate that the proposed method avoids both over-segmentation and under segmentation, and reveal the reliability of bubble size distribution nonparametric density estimation.