基于形态学重建和GMM的球团颗粒图像分割
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TH741 TP391.41

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Image segmentation of pellet particles based on morphological reconstruction and GMM
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    摘要:

    机器视觉技术的发展为颗粒粒径的自动测量提供了一种有效方法,但是,重叠颗粒的图像分割问题仍有待进一步解决。针对这一问题,提出一种基于形态学重建和高斯混合模型的球团颗粒图像分割算法。首先利用似圆度将单独颗粒和重叠颗粒进行区分;根据重叠颗粒图像距离变换特征建立了高斯混合模型;为实现无监督的聚类,采用形态学重建结合聚类有效性指标的方法获得最佳聚类数目,并利用期望极大(EM)算法进行求解;最后采用圆拟合的方法对缺失的球团颗粒轮廓进行重构,实现了对重叠球团颗粒的分割。实验结果表明,该算法能够有效地对重叠颗粒进行分割,分割正确率评价指标AC为 936%,明显优于现有的对比算法,为基于机器视觉的球团颗粒粒径分布测量奠定了基础。

    Abstract:

    The development of machine vision technology provides an effective method for automatic measurement of particle size distribution. However,the overlapping particles in theimageis still difficult to be segmented. To solve this problem, one kind ofpellet image segmentation algorithm based on morphological reconstruction and Gauss mixture model is proposed. To achieve unsupervised clustering, morphological reconstruction combined with clustering validity index isused to obtain the optimal number of clusters.EM algorithm isutilizedto solve this problem. Finally, the missing particle contours arereconstructed by circle fitting method.The segmentation of overlapping pellets is realized. Experimental results show that the algorithm can effectively segment overlapping pellets.The segmentation accuracy evaluation index AC is 936%, which is obviously superior to the compared algorithms. The measurement of particle size distribution based on machine vision is founded.

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刘小燕,吴鑫,孙炜,毛传刚.基于形态学重建和GMM的球团颗粒图像分割[J].仪器仪表学报,2019,40(3):230-238

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  • 在线发布日期: 2022-01-14
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