Standard
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 › fagfællebedømt
Harvard
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
APA
Pedersen, K. S., 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 (s. 362-373). Springer. Lecture notes in computer science Nr. 4485
https://doi.org/10.1007/978-3-540-72823-8_31
Vancouver
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).
https://doi.org/10.1007/978-3-540-72823-8_31
Author
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).
Bibtex
@inproceedings{779074d0b54c11dcbee902004c4f4f50,
title = "Generic maximum likely scale selection",
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.",
author = "Pedersen, {Kim Steenstrup} and Marco Loog and Bo Markussen",
year = "2007",
doi = "10.1007/978-3-540-72823-8_31",
language = "English",
isbn = "978-3-540-72822-1",
series = "Lecture notes in computer science",
publisher = "Springer",
number = "4485",
pages = "362--373",
editor = "Fiorella Sgallari and Almerica Murli and Nikos Paragios",
booktitle = "Scale Space and Variational Methods in Computer Vision",
address = "Switzerland",
note = "null ; Conference date: 30-05-2007 Through 02-06-2007",
}
RIS
TY - GEN
T1 - Generic maximum likely scale selection
AU - Pedersen, Kim Steenstrup
AU - Loog, Marco
AU - Markussen, Bo
N1 - Conference code: 1
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-540-72823-8_31
DO - 10.1007/978-3-540-72823-8_31
M3 - Article in proceedings
SN - 978-3-540-72822-1
T3 - Lecture notes in computer science
SP - 362
EP - 373
BT - Scale Space and Variational Methods in Computer Vision
A2 - Sgallari, Fiorella
A2 - Murli, Almerica
A2 - Paragios, Nikos
PB - Springer
Y2 - 30 May 2007 through 2 June 2007
ER -
