Detection of Shape Anomalies: A Probabilistic Approach Using Hidden Markov Models
Cancun（2008）
摘要
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.
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关键词
shape anomaly,probabilistic approach,image recognition,markov model,shape anomaly detection,rotation invariant manner,one-dimensional representation,generalized segmental,large collection,shape anomaly detection algorithm,hidden markov models,time series,shape anomalies,security of data,anomaly detection,hidden markov model,biomedical imaging,capacitive sensors,dynamic programming,algorithm design and analysis,shape,genetics,time series analysis
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