Froth delineation based on image classification

This paper describes a set of image segmentation algorithms for mineral froth images, based on gray-value valley detection and a kind of image classification. The size, shape, texture and color of froth bubbles are very important pieces of information for production optimization in mineral processing. In order to determine these parameters, bubbles in a froth image first have to be delineated. Froth images display a large variation of image patterns and quality, thus it is difficult to use only a single algorithm for segmenting all images. To achieve successful segmentation the images are first classified into image classes. Then sets of segmentation algorithms are used, based on the different image classes. The segmentation algorithms and classification algorithms have been tested in a laboratory and in industrial on-line systems for froth images, the test results show that they are robust for froth images. The processing speed for the segmentation algorithm is much faster than for a standard morphological segmentation algorithm. The processing accuracy is comparable to manual drawn result. This test shows that the algorithms work satisfactorily.