@inbook {IOPORT.05708353,
    author       = {Connors, Warren A. and Connor, Patrick C. and Trappenberg, Thomas},
    title        = {Detection of mine-like objects using restricted Boltzmann machines.},
    year         = {2010},
    booktitle    = {Advances in artificial intelligence. 23rd Canadian conference on artificial intelligence, Canadian AI 2010, Ottawa, Canada, May 31 -- June 2, 2010. Proceedings},
    isbn         = {978-3-642-13058-8},
    pages        = {362-365},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-642-13059-5_47},
    abstract     = {Summary: Automatic target recognition (ATR) of objects in side scan sonar imagery typically employs image processing techniques (e.g. segmentation, Fourier transform) to extract features describing the objects. The features are used to discriminate between sea floor clutter and targets (e.g. sea mines). These methods are typically developed for a specific sonar, and are computationally intensive. The present work used the Restricted Boltzmann Machine (RBM) to discriminate between images of targets and clutter, achieving a 90\% probability of detection and a 15\% probability of false alarm, which is comparable to the performance of a Support Vector Machine (SVM) and other state-of-the-art methods on the data. The RBM method uses raw image pixels and thus avoids the issue of manually selecting good representations (features) of the data.},
    identifier   = {05708353},
}