Maximum likely scale estimation
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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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
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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