CUED Publications database

Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

Nelson, JDB and Kingsbury, NG (2012) Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting. IET Signal Processing, 6. pp. 484-493. ISSN 1751-9675

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An anomaly detection approach is considered for the mine hunting in sonar imagery problem. The authors exploit previous work that used dual-tree wavelets and fractal dimension to adaptively suppress sand ripples and a matched filter as an initial detector. Here, lacunarity inspired features are extracted from the remaining false positives, again using dual-tree wavelets. A one-class support vector machine is then used to learn a decision boundary, based only on these false positives. The approach exploits the large quantities of 'normal' natural background data available but avoids the difficult requirement of collecting examples of targets in order to train a classifier. © 2012 The Institution of Engineering and Technology.

Item Type: Article
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:33
Last Modified: 05 Feb 2019 12:39
DOI: 10.1049/iet-spr.2011.0070