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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | ter Haar Romeny, B.M. Niessen, W.J. Wilting, J. Florack, L.M.J. |
| Copyright Year | 1994 |
| Description | Author affiliation: 3D Comput. Vision Res. Group, Utrecht Univ. Hospital, Netherlands (ter Haar Romeny, B.M.; Niessen, W.J.; Wilting, J.; Florack, L.M.J.) |
| Abstract | Differentiation is known to be ill-posed in the sense of Hadamard. The theory of regular tempered distributions and the concept of Gaussian convolution filters open the way to a well-posed differentiation process, thereby introducing the notion of scale (or: inverse resolution). There is no a priori fundamental limit to the order of differentiation of images provided they are calculated on a sufficiently high scale (relative to pixel scale and noise correlation width), and provided we have a sufficient dynamic range of intensity values. Constraints in resolution (both in the spatial and in the intensity domain) enforce a scale-dependent restriction to the accuracy with which Gaussian kernels G/sub n/(x; /spl sigma/) can be represented in a physical sense. So at a given scale /spl sigma/ (e.g. in units of the sampling scale) and a given measure of inaccuracy /spl alpha/ there is a maximal order n above which the margin /spl alpha/ is exceeded. In this paper we quantify this relation. |
| Starting Page | 21 |
| Ending Page | 25 |
| File Size | 406715 |
| Page Count | 5 |
| File Format | |
| ISBN | 0818669527 |
| DOI | 10.1109/ICIP.1994.413267 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1994-11-13 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Data mining Spatial resolution Laplace equations Convolution Pixel Apertures Image sampling Computer vision Hospitals |
| Content Type | Text |
| Resource Type | Article |
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