A non-parametric 2D deformable template classifier

Publication: Research - peer-reviewJournal article – Annual report year: 2005

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We introduce an interactive segmentation method for a sea floor survey. The method is based on a deformable template classifier and is developed to segment data from an echo sounder post-processor called RoxAnn. RoxAnn collects two different measures for each observation point, and in this 2D feature space the ship-master will be able to interactively define a segmentation map, which is refined and optimized by the deformable template algorithms. The deformable templates are defined as two-dimensional vector-cycles. Local random transformations are applied to the vector-cycles, and stochastic relaxation in a Bayesian scheme is used. In the Bayesian likelihood a class density function and its estimate hereof is introduced, which is designed to separate the feature space. The method is verified on data collected in Øresund, Scandinavia. The data come from four geographically different areas. Two areas, which are homogeneous with respect to bottom type, are used for training of the deformable template classifier, and the classifier is applied to two areas, which are heterogeneous with respect to bottom type. The classification results are good with a correct classification percent above 94 per cent for the bottom type classes, and show that the deformable template classifier can be used for interactive on-line sea floor segmentation of RoxAnn echo sounder data.
Original languageEnglish
Issue number1
Pages (from-to)81-94
StatePublished - 2005
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ID: 4063272