Depth Data Segmentation Using Robust Estimation in a Hierarchical Data Structure
In this paper a novel segmentation algorithm which fits parametric surfaces to depth data is presented. The method incorporates Robust Statistical Estimation techniques  together with a hierachical data structure. The Robust Statistical techniques are important as they provide both outlier rejection and some tolerance to deviations from modelled th data distribution. The outlier rejection effectively corresponds to constraining the points which enter the fit to lie within a 3D template region. Deviations from commonly assummed model distributions (such as Gaussian noise processes) can occur from many effects including inclusion of two or more physical surfaces in a fitting window. The use of a hierarchy is important as the smallest image windows are likely to contain simple scene structures. Therefore the surface parameters derived from this lowest level are likely to be accurate representations of the window contents. These estimates can then be used to provide strong constraints which allow reliable and increasingly accurate surface estimates to be calculated at higher levels of the hierarchy. In this paper we demonstrate these principles by developing an algorithm which fits planar surfaces to depth data. Results are shown on both synthetic and real depth data images.
KeywordsDepth Data Influence Function Hough Transform Outlier Rejection Sibling Node
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