Recursive square-root filters

Author

Haindl, Michal

Author_Institution

Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czech Republic

Volume

2

fYear

2000

fDate

2000

Firstpage

1014

Abstract

Presents a derivation of recursive square-root filters, which can update either a symmetric positive data gathering matrix or its inversion. These filters differ from usual recursive approaches by their ability not only to supply new data to these matrices but also to simultaneously remove data previously fed to these matrices during their build-up. A condition for stable data removal is proven. Efficient recursive statistics of the least square or the maximum pseudo-likelihood type can be built using these results as it is briefly demonstrated on the colour texture segmentation example

Keywords

filtering theory; image colour analysis; image segmentation; image texture; least squares approximations; matrix inversion; recursive estimation; recursive filters; colour texture segmentation; least square recursive statistics; maximum pseudo-likelihood recursive statistics; recursive square-root filters; symmetric positive data gathering matrix; Data analysis; Equations; Filters; Least squares approximation; Markov random fields; Matrix decomposition; Predictive models; Robustness; Statistics; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2000. Proceedings. 15th International Conference on

Conference_Location

Barcelona

ISSN

1051-4651

Print_ISBN

0-7695-0750-6

Type

conf

DOI

10.1109/ICPR.2000.906246

Filename

906246