应用支持向量机处理岩土材料的细观图像

应用数字图像处理技术提取非均质岩土材料的细观特征是量化其细观结构的有效途径。为提高图像处理的质量和效率,在进行数字图像处理的阈值分割时,采用统计学习理论中的支持向量机分类方法。选取待原始图像的一个矩形区域作为训练样本图像,提取这些样本点的特征与训练目标一起组成训练样本集,通过对训练样本集的学习,生成SVM(support vector machine)分类机,利用SVM分类机提取原始图像中的特征图像。以花岗岩为例,利用该方法提取其细观结构,结果表明,合理选取训练样本和模型参数,可以提高图像处理的准确率和效率,得到最佳的处理结果。

Application of a support vector machine to meso-structural image analysis of geomaterials

Applying digital image processing technology to the extraction of meso-structural features from heterogeneous geomaterials is an effective approach for quantifying meso-structures.To improve the quality and efficiency of image processing,the classification method of the support vector machines(SVM) based on statistics theory was utilized in the threshold segmentation of digital image processing.First,a rectangular region of the original image was selected as the training sample image.The characteristics derived from this sample image and training targets constitute a training sample set.By learning the training sample set,the SVM classifier was produced next.The characteristic image then can be obtained using the SVM classifier.When employing this method to analyze a granitic rock image,the results show that the new method improves the precision as well as the efficiency of image processing.The new method obtains the best processing performance when reasonable training samples and SVM parameters are selected.