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Fractal dimension, wavelet shrinkage and anomaly detection for mine hunting

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2 Author(s)
Nelson, J.D.B. ; Dept. of Stat. Sci., Univ. Coll. London, London, UK ; Kingsbury, N.G.

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.

Published in:

Signal Processing, IET  (Volume:6 ,  Issue: 5 )