Maximum likely scale estimation

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

Standard

Maximum likely scale estimation. / Loog, Marco; Pedersen, Kim Steenstrup; Markussen, Bo.

Deep Structure, Singularities, and Computer Vision. Springer, 2005. s. 146-156 (Lecture notes in computer science, Bind 3753/2005).

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

Harvard

Loog, M, Pedersen, KS & Markussen, B 2005, Maximum likely scale estimation. i Deep Structure, Singularities, and Computer Vision. Springer, Lecture notes in computer science, bind 3753/2005, s. 146-156, First International Workshop in Deep Structure, Singularities, and Computer Vision (DSSCV), Maastricht, Holland, 29/11/2010. https://doi.org/10.1007/11577812_13

APA

Loog, M., Pedersen, K. S., & Markussen, B. (2005). Maximum likely scale estimation. I Deep Structure, Singularities, and Computer Vision (s. 146-156). Springer. Lecture notes in computer science Bind 3753/2005 https://doi.org/10.1007/11577812_13

Vancouver

Loog M, Pedersen KS, Markussen B. Maximum likely scale estimation. I Deep Structure, Singularities, and Computer Vision. Springer. 2005. s. 146-156. (Lecture notes in computer science, Bind 3753/2005). https://doi.org/10.1007/11577812_13

Author

Loog, Marco ; Pedersen, Kim Steenstrup ; Markussen, Bo. / Maximum likely scale estimation. Deep Structure, Singularities, and Computer Vision. Springer, 2005. s. 146-156 (Lecture notes in computer science, Bind 3753/2005).

Bibtex

@inproceedings{4ca614704ce411dd8d9f000ea68e967b,
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. ",
author = "Marco Loog and Pedersen, {Kim Steenstrup} and Bo Markussen",
year = "2005",
doi = "10.1007/11577812_13",
language = "English",
isbn = "978-3-540-29836-6",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "146--156",
booktitle = "Deep Structure, Singularities, and Computer Vision",
address = "Switzerland",
note = "null ; Conference date: 29-11-2010",

}

RIS

TY - GEN

T1 - Maximum likely scale estimation

AU - Loog, Marco

AU - Pedersen, Kim Steenstrup

AU - Markussen, Bo

N1 - Conference code: 1

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

U2 - 10.1007/11577812_13

DO - 10.1007/11577812_13

M3 - Article in proceedings

SN - 978-3-540-29836-6

T3 - Lecture notes in computer science

SP - 146

EP - 156

BT - Deep Structure, Singularities, and Computer Vision

PB - Springer

Y2 - 29 November 2010

ER -

ID: 4941791