Enhanced Robust Vortex Detection

摘要：

We propose to leverage methods of machine learning to enhance robustness of feature detection aIgorithm.First, we use semi-supervised learning to develop strategies for guiding the selective refinement process based on training with the domain expert. Second, we propose to combine several local feature detection algorithm into a single, more robust compound classifier using AdaBoost that produces validated feature detection. The compound classifier would combine the best of all local classifiers as they respond to the underlying physical signal. The specific application of interest is vortex detection in turbulent flows. We applied our algorithms to fluid datasets to illustrate the efficacy of our approach.

关键词： Index Terms-flow visualization vortex detection machine learning.

作者: Li Zhang Xiangxu Meng

作者单位: School of Computer Science and Technology Shandong University Jinan, China

会议类型: 国际会议

会议名称: 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics 第4届智能人机系统与控制论国际会议 IHMSC 2012

会议地点: 南昌

会议语种:英文

页码: 582-585

在线出版日期: 2012-08-26（万方平台首次上网日期，不代表论文的发表时间）