Gaussian scale space from insufficient image information

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedings

Gaussian scale space is properly defined and well-developed for images completely knownand defined on the d dimensional Euclidean space Rd. However, as soon as image information is only partly available, say, on a subset V of Rd, the Gaussian scale space paradigm is not readily applicable and one has to resort to different approaches to come to a scale space on V. Examples are the theory dealing with scale space on Zd ¿ Rd, i.e., discrete scale space; the approach based on the heat equation satisfying certain boundary conditions; and the ad hoc approaches dealing with (hyper)rectangular images, e.g. zero-padding of the area outside of V, or periodic continuation of the image. We propose to solve the foregoing problem for general V from a Bayesian viewpoint. Assuming that the observed image is obtained by linearly sampling a real underlying image that is actually defined on the complete d dimensional Euclidean space, we can infer this latter image and from that image build the scale space. Re-sampling this scale space then gives rise to the scale space on V. Necessary for inferring the underlying image is knowledge on the linear apertures (or receptive field) used for sampling this image, and information on the prior over the class of all images.
TitelScale Space Methods in Computer Vision : 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings
RedaktørerLewis D. Griffin, Martin Lillholm
ISBN (Trykt)978-3-540-40368-5
StatusUdgivet - 2003
Eksternt udgivetJa
Begivenhed4th International Conference on Scale Space Methods in Computer Vision - Isle of Skye, Storbritannien
Varighed: 10 jun. 200312 jun. 2003
Konferencens nummer: 4


Konference4th International Conference on Scale Space Methods in Computer Vision
ByIsle of Skye
NavnLecture notes in computer science

ID: 5581219