Maximum likely scale estimation

Marco Loog; Kim Steenstrup Pedersen; Bo Markussen

doi:10.1007/11577812_13

Maximum likely scale estimation

Marco Loog, Kim Steenstrup Pedersen, Bo Markussen

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.

Originalsprog	Engelsk
Titel	Deep Structure, Singularities, and Computer Vision
Forlag	Springer
Publikationsdato	2005
Sider	146-156
ISBN (Trykt)	978-3-540-29836-6
DOI	https://doi.org/10.1007/11577812_13
Status	Udgivet - 2005
Begivenhed	First International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV) - Maastricht, Holland Varighed: 29 nov. 2010 → … Konferencens nummer: 1

Konference

Konference	First International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV)
Nummer	1
Land/Område	Holland
By	Maastricht
Periode	29/11/2010 → …

Navn	Lecture notes in computer science
Vol/bind	3753/2005
ISSN	0302-9743

Adgang til dokumentet

10.1007/11577812_13

http://www.springerlink.de/content/yk837544xp6k8274/fulltext.pdf

Citationsformater

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title = "Maximum likely scale estimation",

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. ",

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