We propose a novel approach for detection of salient image points
and estimation of their intrinsic scales based on the fractional
Brownian image model. Under this model images are realisations of a
Gaussian random process on the plane. We define salient points as
points that have a locally unique image structure. Such points are
usually sparsely distributed in images and carry important
information about the image content. Locality is defined in terms of
the measurement scale of the filters used to describe the image
structure. Here we use partial derivatives of the image function
defined using linear scale space theory. We propose to detect
salient points and their intrinsic scale by detecting points in
scale-space that locally minimise the likelihood under the model.