Title
Discrete fast algorithms for two-dimensional linear prediction on a polar raster.
Abstract
Discrete generalized split Levinson and Schur algorithms for the two-dimensional linear least-squares prediction problem on a polar raster are derived. The algorithms compute the prediction filter for estimating a random field at the edge of a disk from noisy observations inside the disk. The covariance functions of the random field is assumed to have a Toeplitz-plus-Hankel structure for its radial part and its transverse part. This assumption can be shown to be closely related with some types of random fields, such as isotropic random fields. The algorithms generalized the split Levinson and Schur algorithms in two ways: (1) to two dimensions; and (2) to Toeplitz-plus-Hankel covariances
Year
DOI
Venue
1992
10.1109/78.139250
IEEE Transactions on Signal Processing
Keywords
Field
DocType
covariance function,white noise,biomedical imaging,computational complexity,lattices,two dimensions,image restoration,image processing,random field,prediction algorithms
Isotropy,Mathematical optimization,Random field,Raster graphics,Transverse plane,Algorithm,Linear prediction,Polar,Mathematics,Covariance,Computational complexity theory
Journal
Volume
Issue
ISSN
40
6
1053-587X
Citations 
PageRank 
References 
3
1.65
4
Authors
2
Name
Order
Citations
PageRank
W.-H. Fang1144.65
A. E. Yagle29524.97