@inproceedings {514,
	title = {Recursive multiscale estimation of space-time random fields},
	booktitle = {International Conference on Image Processing},
	year = {1999},
	abstract = {<p>We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more efficiently than the Kalman filter\&$\#$39;s propagation of the error covariances, with a resulting computational complexity of Oscr;(N) and Oscr;(N3/2 ), where N is the number of variables estimated, for 1-D and 2-D dynamic systems, respectively. To further reduce the computational cost, we introduce in this paper a new class of reduced-order spatially-interpolated multiscale models, and demonstrate their use in remote</p>
},
	keywords = {computational complexity, estimation errors, image processing, large-scale dynamic systems, multiscale models, recursive estimation, recursive multiscale estimation, reduced-order spatially-interpolated multiscale models, space-time adaptive processing, space-time random fields},
	doi = {http://dx.doi.org/10.1109/ICIP.1999.823021},
	author = {T T. Ho and P Fieguth and A S. Wilsky}
}
