摘要
为实现岩石薄片图像孔隙识别的自动化,提出了一种基于聚类分割和神经网络相结合的分类识别方法。首先在图像中应用Kmeans聚类分割算法,将岩石图像分割为背景岩石和目标孔隙两类,并分别提取足够特征进行分类测试,效果良好。其次选100幅岩石图像,每组5幅图像共20组,每组200个数据进行验证。实验表明,建立好的概率神经网络可以准确分类识别出目标孔隙,识别平均正确率为95.12%,已达到实际应用需要。
In order to realize the recognition automation of rock section pore images, a method combined Kmeans clustering with probabilistic neural network is proposed and applied to rock section images. Firstly, Kmeans cluste- ring is used as segmentation algorithm, the rock images are divided into two types and extracted enough features and it is shown good classification effect on testing dataset. Secondly, 100 pieces of rock image section are used as vali- dation dataset, including 5 images of each 20 groups, a group has 200 data samplings. Experiments show that the probabilistic neural network can be used as rock texture classifier, the average correct classification rate is around 95.12%, which can meet the practical application needs.
出处
《科学技术与工程》
北大核心
2013年第31期9231-9235,共5页
Science Technology and Engineering
基金
国家自然科学基金(40872087)资助
关键词
Kmeans聚类
概率神经网络
岩石薄片图像
模式识别
color image segmentationimage pattern recognitionkmeans clustering probabilistic neural network rock section
