Eric C. Lundberg

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Tracking linear features through tensor field datasets is an open research problem with widespread utility in medical and engineering disciplines. Existing tracking methods, which consider only the preferred local diffusion direction as they propagate, fail to accurately follow features as they enter regions of local complexity. This shortcoming is a result(More)
Streamline advection has proven an eeective method for visualizing vector ow eld data. Traditional streamlines do not, however, provide for investigating the coarser-grained features of complex datasets, such as the white matter tracts in the brain or the thermal conveyor belts in the ocean. In this paper, we introduce a cohesive advection primitive, called(More)
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