@inproceedings {511,
	title = {Foveated multiscale models for large-scale estimation},
	booktitle = {International Conference on Image Processing},
	year = {1999},
	abstract = {<p>Efficient, large-scale estimation methods such as nested dissection or multiscale estimation rely on a divide-and-conquer strategy, in which a statistical problem is conditionally broken into smaller pieces. This conditional decorrelation is not possible for arbitrarily large problems due to issues of computational complexity and numerical stability. Given the growing interest in global-scale-remote sensing problems (or even three-dimensional problems), in this summary we develop a class of estimators with more promising asymptotic computational properties</p>
},
	keywords = {asymptotic computational properties, computational complexity, conditional decorrelation, decorrelation, divide and conquer methods, divide-and-conquer strategy, foveated multiscale models, global-scale-remote sensing problems, image processing, large-scale estimation methods, multiscale estimation, nested dissection, numerical stability, statistical problem, three-dimensional problems},
	doi = {http://dx.doi.org/10.1109/ICIP.1999.823022},
	author = {P Fieguth}
}
@inproceedings {463,
	title = {A Statistical Multiscale Approach To Mapping Altimetric Data},
	booktitle = {Neural and Stochastic Methods in Image and Signal Processing III},
	year = {1994},
	publisher = {SPIE},
	organization = {SPIE},
	abstract = {<p>A challenging problem in oceanography is the dense estimation of the surface of the ocean in a statistically meaningful manner, given sparse and irregularly sampled measurements of the surface. A previously developed highly efficient multiscale estimation framework is shown to be an appropriate tool for this task, and we demonstrate the manner of application and present experimental results. The algorithm is capable of computing 250,000 surface estimates of the ocean with error statistics in five seconds on a Sparc workstation.</p>
},
	keywords = {assimiliation, multiscale algorithms, multiscale estimation, smoothing},
	doi = {http://dx.doi.org/10.1117/12.179217},
	url = {http://link.aip.org/link/?PSI/2304/94/1},
	author = {A S. Willsky and P Fieguth and W C. Karl},
	editor = {S-S Chen}
}
