maximize


@article{MiPrRu03,
  author = {Mikula, K. and Preu{\ss}er, T. and Rumpf, M.},
  title = {Morphological Image Sequence Processing},
  journal = {Computing and Visualization in Science},
  year = {2003},
  volume = {6},
  pages = {197--209},
  number = {4},
  abstract = {We present a morphological multi-scale method for image sequence processing,
	which results in a truly coupled spatio-temporal anisotropic diffusion.
	The aim of the method is not to smooth the level sets of single frames
	but to denoise the whole sequence while retaining geometric features
	such as spatial edges and highly accelerated motions. This is obtained
	by an anisotropic spatio-temporal level set evolution, where the
	additional artificial time variable serves as the multi -scale parameter.
	The diffusion tensor of the evolution depends on the morphology of
	the sequence, given by spatial curvatures of the level sets and the
	curvature of trajectories (=acceleration) in sequence-time. We discuss
	different regularization techniques and describe an operator splitting
	technique for solving the problem. Finally we compare the new method
	with existing multi-scale image sequence processing methodologies.},
  pdf = {http://numod.ins.uni-bonn.de/research/papers/public/MiPrRu03.pdf 1}
}