Generic maximum likely scale selection

Authors:
Kim Steenstrup Pedersen;Marco Loog;Bo Markussen
Affiliations:
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark;Department of Computer Science, University of Copenhagen, Copenhagen, Denmark and Nordic Bioscience A/S, Herlev, Denmark;Department of Natural Sciences, Royal Veterinary and Agricultural University, Frederiksberg, Denmark
Venue:
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Year:
2007

Citing 6
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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.