Scale-space from nonlinear filters

Author

Bangham, Andrew J. ; Ling, Paul ; Harvey, Richard

Author_Institution

Sch. of Inf. Syst., East Anglia Univ., Norwich, UK

fYear

1995

fDate

20-23 Jun 1995

Firstpage

163

Lastpage

168

Abstract

Decomposition by extrema is put into the context of linear vision systems and scale-space. One dimensional discrete M- and N-sieves neither introduce new edges as the scale increases nor create new extrema. They share this property with diffusion based filters. Furthermore M- and N-sieve algorithms are extremely fast with order complexity n. Used to decompose an image, the resulting granularity is appropriate for pattern recognition

Keywords

computational complexity; computer vision; image recognition; nonlinear filters; 1D discrete M-sieves; 1D discrete N-sieves; diffusion based filters; extrema decomposition; granularity; image decomposition; linear vision systems; nonlinear filters; order complexity; pattern recognition; scale-space; Convolution; Image analysis; Image processing; Image recognition; Information systems; Layout; Machine vision; Nonlinear filters; Signal analysis; Smoothing methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision, 1995. Proceedings., Fifth International Conference on

Conference_Location

Cambridge, MA

Print_ISBN

0-8186-7042-8

Type

conf

DOI

10.1109/ICCV.1995.466791

Filename

466791

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3311413