Segmentation algorithm of coal slurry foam with double-point directional extension based on the improved FCM clustering algorithm

In flotation production, the visual surface information of the flotation foam reflects the flotation effects, which are closely related to the flotation conditions and directly reflect the degree of mineralization of the foam layer. In this study, it was proposed a novel and efficient segmentation algorithm to extract the edge information of slime bubbles, as the boundaries are typically blurred and difficult to segment, due to the slime bubbles sticking to each other in the slime flotation foam image. First, the improved clustering algorithm and image morphology operation were used to extract the edges of the foam spots. Second, the image morphological operations were used as a starting point to look around the foam edge points. The pseudo-edge points were then removed using a region and spatial removal algorithm. Finally, the foam edges were extracted using the double-point directed expansion algorithm. A new criterion was proposed for segmentation effect determination based on the particularity of the segmented object. The feasibility and effectiveness of the foam segmentation method were investigated by comparative experiments. The experimental results showed that the proposed algorithm could obtain the foam surface properties more accurately and provide effective guidance for flotation production.