Maximum likely scale estimation

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningpeer review

3 Citationer (Scopus)

Abstract

A maximum likelihood local scale estimation principle is presented. An actual implementation of the estimation principle uses second order moments of multiple measurements at a fixed location in the image. These measurements consist of Gaussian derivatives possibly taken at several scales and/or having different derivative orders.
Although the principle is applicable to a wide variety of image models, the main focus here is on the Brownian model and its use for scale selection in natural images. Furthermore, in the examples provided, the simplifying assumption is made that the behavior of the measurements is completely characterized by all moments up to second order.
OriginalsprogEngelsk
TitelDeep Structure, Singularities, and Computer Vision
ForlagSpringer
Publikationsdato2005
Sider146-156
ISBN (Trykt)978-3-540-29836-6
DOI
StatusUdgivet - 2005
BegivenhedFirst International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV) - Maastricht, Holland
Varighed: 29 nov. 2010 → …
Konferencens nummer: 1

Konference

KonferenceFirst International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV)
Nummer1
Land/OmrådeHolland
ByMaastricht
Periode29/11/2010 → …
NavnLecture notes in computer science
Vol/bind3753/2005
ISSN0302-9743

Citationsformater