Shape registration with directional data. (July 2018) - Grogan, Mairéad; Dahyot, Rozenn
Highlights: We extend the L2 registration framework to include directional data. We model the directional data using von Mises Fisher kernels. We propose 4 cost functions for shape registration based on this modelling. Our method compares well with State of the Art techniques. Abstract: We propose several cost functions for registration of shapes encoded with Euclidean and/or non-Euclidean information (unit vectors). Our framework is assessed for estimation of both rigid and non-rigid transformations between the target and model shapes corresponding to 2D contours and 3D surfaces. The experimental results obtained confirm that using the combination of a point's position and unit normal vector in a cost function can enhance the registration results compared to state of the art methods.