Edge features and anomaly detection for 3D models
Xiaoyun Chen · Computer Engineering and Applications Journal · 2011
Aiming at problems in identification and detection of 3D models,a new method of anomaly detection based on edge features for 3D models is presented.Each three-dimensional model is expressed as a time series through the edge features.Then the obtained time series dataset are clustered using the isodata algorithm.Anomalies are detected by partitioning the dataset twice using the clustering results.It partitions the dataset into two subsets,the preparatory norm set and the preparatory anomaly set,and then the final anomalies are further filtered from the preparatory anomaly set.Experiment results show better performance of the proposed method compared with anomaly detection methods based on distance,neighbourhood or relative density.Under certain conditions,it is also better than density based anomaly detection.