Froth delineation based on image classification
Froth delineation based on image classification
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DOI：
10.1016/j.mineng.2003.07.014
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发表时间：
2003-11
影响因子：
4.8
通讯作者：
Wen Wang;F. Bergholm;B. Yang
中科院分区：
文献类型：
--
作者：
Wen Wang;F. Bergholm;B. Yang
本文提出了一种基于灰度值谷检测和一种图像分类的矿物泡沫图像分割算法。泡沫的大小、形状、结构和颜色是选矿生产优化的重要信息。为了确定这些参数，首先必须描绘泡沫图像中的气泡。泡沫图像显示出图像模式和质量的巨大变化，因此很难使用单一算法对所有图像进行分割。为了实现成功的分割，首先将图像分类为图像类。然后，根据不同的图像类别，使用一组分割算法。在实验室和工业在线系统中对泡沫图像的分割算法和分类算法进行了测试，测试结果表明它们对泡沫图像具有鲁棒性。该分割算法的处理速度比标准形态分割算法快得多。加工精度可与手工绘制结果相媲美。实验结果表明，该算法运行良好。
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.