maximize


@article{DrRuSt03,
  author = {Robert Strzodka and Marc Droske and Martin Rumpf},
  title = {Fast Image Registration in {DX9} Graphics Hardware},
  journal = {Journal of Medical Informatics and Technologies},
  year = {2003},
  volume = {6},
  pages = {43--49},
  month = {Nov},
  abstract = {The analysis of image time series requires a correlation of the information
	between two images. The gradient flow registration is a method for
	correlating this information by successively minimizing an appropriate
	energy along its gradient. A graphics hardware implementation of
	this approach to image registration is presented. The gradient flow
	formulation makes use of a robust multi-scale regularization, an
	efficient multi-grid solver and an effective time-step control. The
	locality of the involved operations implies a data-flow which is
	very well suited for an acceleration in the streaming architecture
	of the DX9 graphics hardware. Therefore the implementation promises
	very high performance, however the appropriate graphics hardware
	is not available before February. Currently the examples have been
	computed on a emulator, but the implementation will run unchanged
	on the soon released graphics hardware.},
  pdf = {http://numod.ins.uni-bonn.de/research/papers/public/DrRuSt03.pdf 1},
  html = {http://numerik.math.uni-duisburg.de/people/strzodka/projects/IP/}
}