• Generic maximum likely scale s...

Treffer: Generic maximum likely scale selection

Title:
Generic maximum likely scale selection
Authors:
Sgallari, Fiorella, Murli, Almerica, Paragios, Nikos, Pedersen, Kim Steenstrup, Loog, Marco, Markussen, Bo
Source:
Pedersen , K S , Loog , M & Markussen , B 2007 , Generic maximum likely scale selection . in F Sgallari , A Murli & N Paragios (eds) , 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 , no. 4485 , pp. 362-373 ,  International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2007) , Ischia , Italy , 30/05/2007 .
Publisher Information:
Springer 2007
Document Type:
E-Ressource Electronic Resource
Index Terms:
contributionToPeriodical
URL:
https://researchprofiles.ku.dk/da/publications/generic-maximum-likely-scale-selection(779074d0-b54c-11dc-bee9-02004c4f4f50).html
https://doi.org/10.1007/978-3-540-72823-8_31
Availability:
Open access content. Open access content
info:eu-repo/semantics/restrictedAccess
Note:
English
Other Numbers:
DAV oai:pure.atira.dk:publications/779074d0-b54c-11dc-bee9-02004c4f4f50
urn:ISBN:978-3-540-72822-1
1322544397
Contributing Source:
UNIV OF COPENHAGEN
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1322544397
Database:
OAIster

Weitere Informationen

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.