Automatic ore image segmentation using mean shift and watershed transform

A. Amankwah, C. Aldrich
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引用次数: 17

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.
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基于均值移位和分水岭变换的矿石图像自动分割
在本文中,我们提出了一种新的分割矿石图像的方法,专门用于估计输送带上矿石的粒度分布。分割系统采用均值移位和分水岭算法。均值移位算法用于识别图像数据的概率密度函数的特定模式的像素簇。然后使用像素簇生成分水岭变换和阴影区域的标记。实验结果表明,该算法不仅比标准方法更快,而且具有更强的鲁棒性。
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