Automatic ore image segmention using mean shift and watershed transform

Abstract

In this paper, we present a novel method for segmenting ore images specifically for estimating the size distribution of ore material on conveyer belt. The segmentation system uses the mean shift and watershed algorithm. The mean shift algorithm is used to identify pixel clusters of particular modes of the probability density function of the image data. The pixel clusters are then used to generate markers for the watershed transform and shadow areas in ore image. Experimental results show that the proposed algorithm is not only faster than the standard methods but also more robust.