Detection of shape anomalies: A probabilistic approach using hidden Markov Models

We study the problem of detecting the shape anomalies In this paper. Our shape anomaly detection algorithm is performed on the one-dimensional representation (time series) of shapes, whose similarity Is modeled by a generalized segmental hidden Markov model (HMM) under a scaling, translation and rotation invariant manner. Experimental results show that our proposed approach can find shape anomalies in a large collection of shapes effectively and efficiently. © 2008 IEEE