Generic maximum likely scale selection

Kim Steenstrup Pedersen; Marco Loog; Bo Markussen

doi:10.1007/978-3-540-72823-8_31

Generic maximum likely scale selection

Kim Steenstrup Pedersen, Marco Loog, Bo Markussen

Datalogisk Institut

1 Citationer (Scopus)

Abstract

The fundamental problem of local scale selection is addressed by
means of a novel principle, which is based on maximum likelihood
estimation. The principle is generally applicable to a broad
variety of image models and descriptors, and provides a generic
scale estimation methodology.

The focus in this work is on applying this selection principle
under a Brownian image model. This image model provides a simple
scale invariant prior for natural images and we provide
illustrative examples of the behavior of our scale estimation on
such images. In these illustrative examples, estimation is based
on second order moments of multiple measurements outputs at a
fixed location. These measurements, which reflect local image
structure, consist in the cases considered here of Gaussian
derivatives taken at several scales and/or having different
derivative orders.

Originalsprog	Engelsk
Titel	Scale Space and Variational Methods in Computer Vision : First International conference , SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings
Redaktører	Fiorella Sgallari, Almerica Murli, Nikos Paragios
Antal sider	12
Forlag	Springer
Publikationsdato	2007
Sider	362-373
ISBN (Trykt)	978-3-540-72822-1
DOI	https://doi.org/10.1007/978-3-540-72823-8_31
Status	Udgivet - 2007
Begivenhed	International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007) - Ischia, Italien Varighed: 30 maj 2007 → 2 jun. 2007 Konferencens nummer: 1

Konference

Konference	International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007)
Nummer	1
Land/Område	Italien
By	Ischia
Periode	30/05/2007 → 02/06/2007

Navn	Lecture notes in computer science
Nummer	4485
ISSN	0302-9743

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10.1007/978-3-540-72823-8_31

Citationsformater

Generic maximum likely scale selection. / Pedersen, Kim Steenstrup; Loog, Marco; Markussen, Bo.
Scale Space and Variational Methods in Computer Vision: First International conference , SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings. red. / Fiorella Sgallari; Almerica Murli; Nikos Paragios. Springer, 2007. s. 362-373 (Lecture notes in computer science; Nr. 4485).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Pedersen, KS, Loog, M & Markussen, B 2007, Generic maximum likely scale selection. i F Sgallari, A Murli & N Paragios (red), Scale Space and Variational Methods in Computer Vision: First International conference , SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings. Springer, Lecture notes in computer science, nr. 4485, s. 362-373, International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007), Ischia, Italien, 30/05/2007. https://doi.org/10.1007/978-3-540-72823-8_31

Pedersen KS, Loog M, Markussen B. Generic maximum likely scale selection. I Sgallari F, Murli A, Paragios N, red., Scale Space and Variational Methods in Computer Vision: First International conference , SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings. Springer. 2007. s. 362-373. (Lecture notes in computer science; Nr. 4485). doi: 10.1007/978-3-540-72823-8_31

Pedersen, Kim Steenstrup ; Loog, Marco ; Markussen, Bo. / Generic maximum likely scale selection. Scale Space and Variational Methods in Computer Vision: First International conference , SSVM 2007, Ischia, Italy, May 30 - June 2, 2007. Proceedings. red. / Fiorella Sgallari ; Almerica Murli ; Nikos Paragios. Springer, 2007. s. 362-373 (Lecture notes in computer science; Nr. 4485).

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abstract = "The fundamental problem of local scale selection is addressed bymeans of a novel principle, which is based on maximum likelihoodestimation. The principle is generally applicable to a broadvariety of image models and descriptors, and provides a genericscale estimation methodology.The focus in this work is on applying this selection principleunder a Brownian image model. This image model provides a simplescale invariant prior for natural images and we provideillustrative examples of the behavior of our scale estimation onsuch images. In these illustrative examples, estimation is basedon second order moments of multiple measurements outputs at afixed location. These measurements, which reflect local imagestructure, consist in the cases considered here of Gaussianderivatives taken at several scales and/or having differentderivative orders.",

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Generic maximum likely scale selection

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