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Anomaly detection in sonar images based on wavelet domain noncausal AR-ARCH random field modeling

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2 Author(s)
Saman Mousazadeh ; Faculty of Electrical Engineering, Technion - Israel Institute of Technology ; Israel Cohen

In this paper we introduce a novel anomaly detection method in sonar images based on noncausal autoregressive-autoregressive conditional heteroscedasticity (AR-ARCH) model. The background of the sonar image in the wavelet domain is modeled by a noncausal AR-ARCH model. Matched subspace detector (MFD) is used for detecting the anomaly in the image. The proposed method is computationally efficient and is robust to the orientation variation of the image, compared to competing method.

Published in:

Electrical and Electronics Engineers in Israel (IEEEI), 2010 IEEE 26th Convention of

Date of Conference:

17-20 Nov. 2010