maximize


@inproceedings{DiRu00,
  author = {Diewald, U. and Rumpf, M.},
  title = {Visualization of Principal Curvature Directions by Anisotropic Diffusion},
  booktitle = {Vision, Modeling and Visualization},
  year = {2000},
  editor = {Girod, B. and Greiner, G. and Niemann, H. and Seidel, H.-P.},
  pages = {293--301},
  abstract = {Anisotropic diffusion is known to be a powerful tool in image processing.
	It enables the smoothing of initially noisy images while still retaining,
	respectively sharpening edges and enhancing features. Here recent
	results in the context of vector field visualization are expanded
	to non Euclidean domains. The aim is to graphically represent vector
	field data on two dimensional surfaces in an intuitively understandable
	way. Furthermore the multiscale properties of the approach support
	a scale of resolutions, ranging from detailed flow representation
	to a coarse overview of field data. Here an initial noisy image intensity
	is smoothed along integral lines, whereas the image is mainly sharpened
	in the orthogonal direction. The method is based on a continuous
	model and requires the solution of a parabolic PDE problem on manifolds.
	It is discretized by finite elements on surface triangulations only
	in the final implementational step. Applications are shown for principal
	directions of curvature on general surfaces.},
  pdf = {http://numod.ins.uni-bonn.de/research/papers/public/DiRu00.pdf 1}
}