The Method Of Flotation Froth Image Segmentation Based On Threshold Level Set Method

Flotation is a most extensive mineral processing method in mineral mental refining application. Its froth surface characteristics such as shape, size and so on are the key to judge the mineral quality. Recently, flotation process mainly depends on operators by manipulating the operational parameters, based on observations of froth conditions. In the case of various mineral resources, however, the manual observation has subjectivity and casualness. Hence the optimal operation of flotation process would be significantly affected. Therefore, digital picture processing technique is introduced into the flotation process which is a so important thing.Computer vision instead of human vision will not only strengthen the application of digital image technique in mineral processing, but also promote the development of new mineral processing technology which has very important significance to realize the concentrator intelligent control. Recently, flotation froth image segmentation method at home and abroad mainly concentrates edge exploration method and watershed segmentation method, due to edge exploration method claims to the high light intensity, so the accuracy of the algorithm will be greatly influenced. Although watershed segmentation method avoid this issue, it is so easy to cause the problem of under-segmentation and incorrect segmentation that the accuracy of segmentation decreases.In order to solve the shortcomings arising from conventional flotation bubble image segmentation method, a novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of under-segmentation and incorrect segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.