maximize


@article{DrRuSt04,
  author = {R. Strzodka and M. Droske and M. Rumpf},
  title = {Image Registration by a Regularized Gradient Flow - A Streaming Implementation
	in {DX9} Graphics Hardware},
  journal = {Computing},
  year = {2004},
  volume = {73},
  pages = {373--389},
  number = {4},
  abstract = {The presented image registration method uses a regularized gradient
	flow to correlate the intensities in two images. Thereby, an energy
	functional is successively minimized by descending along its regularized
	gradient. The gradient flow formulation makes use of a robust multi-scale
	regularization, an efficient multi-grid solver and an effective time-step
	control. The data processing is arranged in streams and mapped onto
	the functionality of a stream processor. This arrangement automatically
	exploits the high data parallelism of the problem, and local data
	access helps to maximize throughput and hide memory latency. Although
	dedicated stream processors exist, we use a DX9 compatible graphics
	card as a stream architecture, because of its ideal performance-price
	ratio, which will allow the use such fast implementations in any
	PC. The new floating point number formats guarantee a sufficient
	accuracy of the algorithm and eliminate afore existing concerns about
	the use of graphics hardware for medical computing. Therefore, the
	implementation achieves reliable results at very high performance,
	registering two $257^2$ in approx. $3$sec, such that it could be
	used as an interactive tool in medical image analysis.},
  pdf = {http://numod.ins.uni-bonn.de/research/papers/public/DrRuSt04.pdf 1},
  html = {http://numerik.math.uni-duisburg.de/people/strzodka/projects/IP/},
  publisher = {Springer-Verlag}
}