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Automated approach to classification of mine-like objects using multiple-aspect sonar images

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In this paper, the detection of mines or other objects on the seabed from multiple side-scan sonar views is considered. Two frameworks are provided for this kind of classification. The first framework is based upon the Dempster–Shafer (DS) concept of fusion from a single-view kernel-based classifier and the second framework is based upon the concepts of multi-instance classifiers. Moreover, we consider the class imbalance problem which is always presents in sonar image recognition. Our experimental results show that both of the presented frameworks can be used in mine-like object classification and the presented methods for multi-instance class imbalanced problem are also effective in such classification.
  • Faculty of Computer Science Dalhousie University, Canada
  • Faculty of Computer Science Dalhousie University, Canada
  • School of Electrical Engineering & Computer Science University of Ottawa, Canada
  • Faculty of Computer Science, Dalhousie University, Canada Institute of Computer Science, Polish Academy of Sciences, Poland
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