基于Mean shift算法的煤岩分界识别

基金项目:

国家自然科学基金重点资助项目（51134024）

国家高技术研究发展计划（863计划）资助项目（2012AA062203）

详细信息

Coal-rock interface recognition based on Mean shift algorithm

1.
School of Mechanical Electronic and Information Engineering, China University of Ming and Technology (Beijing), Beijing 100083, China
2.
School of Mechanical-Electrical Engineering, North China Inst

摘要: 提出了一种基于Mean shift算法的煤岩分界识别方案。首先介绍了Mean shift算法原理，通过关联图像的像素位置向量和灰度值构建了一个空间联合域；然后给出了适用于煤岩图像分割的带宽参数选择方法，以去除虚假孤立区域和杂散边界；最后利用煤岩图像的人造边界和真实边界进行仿真，结果表明Mean shift算法较K-means算法能更准确地获得煤岩分界线。
- 煤岩分界 /
- 煤岩识别 /
- Mean shift算法 /
- 图像分割
Abstract: A coal-rock interface recognition scheme was proposed which was based on Mean shift algorithm. Firstly, principle of Mean shift algorithm was introduced. The pixel position vector and gray value were concatenated to create a joint spatial-range domain. In order to remove isolated areas and spurious boundaries, a selection method of bandwidth parameters in Mean shift were given, which was applicable to coal-rock image segmentation. Simulation results using synthetic and real coal-rock images show Mean shift algorithm can get coal-rock boundary more accurately than K-means algorithm.
- coal-rock interface /
- coal-rock recognition /
- Mean shift algorithm /
- image segmentatio