Automatic ore image segmentation using mean shift and watershed transform

Author

Amankwah, Anthony ; Aldrich, Chris

Author_Institution

Process Eng. Dept., Univ. of Stellenbosch, Stellenbosch, South Africa

fYear

2011

fDate

19-20 April 2011

Firstpage

1

Lastpage

4

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.

Keywords

conveyors; image segmentation; mineral processing; minerals; particle size; probability; transforms; automatic ore image segmentation; conveyer belt; image data; mean shift algorithm; ore material; pixel clusters; probability density function; segmentation system; size distribution; standard methods; watershed transform; Algorithm design and analysis; Clustering algorithms; Estimation; Image segmentation; Pixel; Software algorithms; Transforms; Mean Shift; Ore size distribution estimation; Watershed Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Radioelektronika (RADIOELEKTRONIKA), 2011 21st International Conference

Conference_Location

Brno

Print_ISBN

978-1-61284-325-4

Type

conf

DOI

10.1109/RADIOELEK.2011.5936391

Filename

5936391