工业CT图像亚体素表面检测算法研究

基金项目:

自然科学基金(项目编号50375126)

航空科学基金(项目编号04I53069)资助

详细信息

Research on Subvoxel Surface Detection Algorithms of ICT Images

1. The key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Xian 710072, China;
2. Northwestern Polytechnical University, Xian 710072, China

摘要: 针对ICT图像序列,研究了基于Facet模型和基于矩的亚体素表面检测算法,并通过引入基于Otsu的阈值分割预处理环节,大大减少了待处理体素的数目,在很大程度上提高了原始算法的处理速度。最后在对航空发动机叶片仿真数据的实验中,对比了算法处理效果,结果表明两算法检测精度均可达1/5个像素以内,预处理环节的引入可将原始算法速度提高约4倍。
- 工业CT /
- 亚体素表面检测 /
- Facet模型 /
- 几何矩
Abstract: For ICT image sequence, two kinds of subvoxel surface detection algorithms: facet model-based method and moment-based method are studied. To solve the problem of large computation of both algorithms, a pre-processing procedure based on Otsu threshold segmentation is proposed, which can reduce the number of surface voxel candidates a lot and accelerate the original one greatly. Finally, we compare the two subvoxel edge detection algorithms in the experiment on aeroengine blade simulation data. The results show that both methods can achieve less than 1/5th pixel accuracy and the introduction of the pre-processing procedure can increase the speed about 4 times.
- industrial computer tomography (ICT) /
- subvoxel surface detection /
- facet model /
- geometric moment