摘要：It is difficult to exact accurate bubble size and to make image recognition more reliable for forth image of coal floatation because of low contrast and blurry edges in froth image. An improved method of getting outside dividing lines in watershed segmentation was proposed. In binarization image processing, threshold was optimized applying particle swarm optimization algorithm(PSO)combining with 2-D maximum entropy based gray level co-occurrence matrix. After distance transform, outside dividing lines have been exacted by watershed segmentation. By comparison with Otsu method, the segmentation results have shown that the gotten external watershed markers are relatively accurate, reasonable. More importantly, under segmentation and over segmentation were avoided using the improved method. So it can be proved that extraction algorithm of outside dividing lines based on PSO is effective in image segmentation
关键词：Froth Image in Coal Floatation;Threshold Optimization;Particle Swarm Optimization Algorithm;On-Class Variance Maximum Otsu Method;Distance Transform;Image Segmentation
