Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Stochastic models for generic images
Quarterly of Applied Mathematics
Probability Models for Clutter in Natural Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Properties of Brownian image models in scale-space
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
A scale invariant covariance structure on jet space
DSSCV'05 Proceedings of the First international conference on Deep Structure, Singularities, and Computer Vision
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We introduce functionals on image spaces that can be readily interpreted as image priors, i.e., probability distributions expressing one's uncertainty before having observed any (image) data. However, as opposed to previous work in this area, not the actual images are considered but their observed version, i.e., we assume the images are obtained be means of a linear aperture, which is typically taken to be Gaussian. More specifically, we consider those functionals that are invariant under blurring of the observed images and the main aim is to fully describe the class of admissible functionals under these assumptions. As it turns out, this class of 'priors' is rather large and adding additional constraints may be considered to restrict the possible solutions.