基于多尺度分析及图论归一化割的矿岩颗粒图像分割及应用

Rock Particle Image Segmentation on Multi-scale and Normalized Cut

为克服矿岩颗粒图像分割中的欠分割和过分割现象,基于矿岩颗粒图像的特性,提出一种结合多尺度分析与图论的矿岩颗粒图象分割算法。该算法首先利用图像多尺度变换缩小图像,以去噪和减少数据量,再利用改进的图论归一化割(Normalized Cut)算法来分割矿岩颗粒图像。对多种类型的矿岩颗粒图像进行了分割实验,并与阈值、分水岭、聚类分析及FCM等常用的分割方法进行比较。实验结果证明,研究的算法对岩石颗粒图像具有优于其它分割算法的效果,新算法应用到了大型露天矿爆堆中的岩石块度测量与分析,效果令人满意。

Because of the complex structure of the rock images,using the general image processing methods to segment the ore-bearing rock particle images may cause uneven,owe segmentation and over segmentation phenomenon.In order to improve the accuracy of rock image segmentation,a kind of ore image segmentation algorithm was proposed based on graph theory.In this algorithm,the first step was to reduce images by multi-scale analysis,then,the ore image was segmented by using Normalized Cut.Various categories of orebearing rocks were segmented by NCut and the segmentation images were compared with the segmentation results of traditional image processing methods such as threshold,watershed and clustering analysis,FCM and other processing methods in the experiment.The result of the experiment showed that for some specific rocks,the new algorithm is better than the traditional ones.