Gaussian scale space from insufficient image information
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Gaussian scale space from insufficient image information. / Loog, Marco; Lillholm, Martin; Nielsen, Mads; Viergever, Max A.
Scale Space Methods in Computer Vision: 4th International Conference, Scale Space 2003 Isle of Skye, UK, June 10–12, 2003 Proceedings. red. / Lewis D. Griffin; Martin Lillholm. Springer, 2003. s. 757-769 (Lecture notes in computer science, Bind 2695/2003).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Gaussian scale space from insufficient image information
AU - Loog, Marco
AU - Lillholm, Martin
AU - Nielsen, Mads
AU - Viergever, Max A.
N1 - Conference code: 4
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
U2 - 10.1007/3-540-44935-3_53
DO - 10.1007/3-540-44935-3_53
M3 - Article in proceedings
SN - 978-3-540-40368-5
T3 - Lecture notes in computer science
SP - 757
EP - 769
BT - Scale Space Methods in Computer Vision
A2 - Griffin, Lewis D.
A2 - Lillholm, Martin
PB - Springer
Y2 - 10 June 2003 through 12 June 2003
ER -
ID: 5581219