author = {Preu{\ss}er, T. and Rumpf, M.},
  title = {Extracting Motion Velocities from 3{D} Image Sequences},
  booktitle = {SPIE Conference on Visualization and Data Analysis},
  year = {2003},
  abstract = {Recent image machinery delivers sequences of large scale three-dimensional
	(3D) images with a considerably small sampling width in time. In
	medical as well as in engineering applications the interest lies
	in underlying deformation, growth or motion phenomena. A robust method
	is presented to extract motion velocities from such image sequences.
	To avoid an ill-posedness of the problem one has to restrict the
	study to certain motion types, which are related to the concrete
	application. The derived formulas for the motion velocities clearly
	reflect the geometry of the motion. Robustness of the presented implementation
	is based on local regularizations in space-time. Thereby geometric
	quantities on the image sequences are evaluated on the local regularizations.
	Examples outline the potential of the proposed method in medical
	applications (3D ultrasound sequences) and experimental fluid dynamics
	(3D flow in porous media). As an improved regularization approach
	an effective denoising method based on anisotropic geometric diffusion
	for 3D data sets is discussed, which respects important features
	on levelsets such as edges and corners and accelerated motions and
	preserves them during the smoothing process. Its application as a
	pre-processing step turns out to be especially advisable for image
	sequences with a considerably small signal to noise ratio.},
  pdf = { 1},
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