Abstract
Steerable filters, as developed by Freeman and Adelson, are a class of rotation-invariant linear operators that may be used to analyze local orientation patterns in imagery. The most common examples of such operators are directional derivatives of Gaussians and their 2-D Hilbert transforms. The inherent symmetry of these filters produces an orientation response that is periodic with period π, even when the underlying image structure does not have such symmetry. This problem may be alleviated by reconsidering the full class of steerable filters. In this paper, we develop a family of even- and odd- symmetric steerable filters that have a spatially asymmetric 'wedge-like' shape and are optimally localized in their orientation response. Unlike the original steerable filters, these filters are not based on directional derivatives and the Hilbert transform relationship is imposed on their angular components. We demonstrate the ability of these filters to properly represent oriented structures.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE International Conference on Computer Vision |
| Editors | Anon |
| Publisher | IEEE |
| Pages | 189-194 |
| Number of pages | 6 |
| State | Published - 1995 |
| Event | Proceedings of the 5th International Conference on Computer Vision - Cambridge, MA, USA Duration: Jun 20 1995 → Jun 23 1995 |
Other
| Other | Proceedings of the 5th International Conference on Computer Vision |
|---|---|
| City | Cambridge, MA, USA |
| Period | 6/20/95 → 6/23/95 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Steerable wedge filters. / Simoncelli, Eero; Farid, H.
IEEE International Conference on Computer Vision. ed. / Anon. IEEE, 1995. p. 189-194.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Steerable wedge filters
AU - Simoncelli, Eero
AU - Farid, H.
PY - 1995
Y1 - 1995
N2 - Steerable filters, as developed by Freeman and Adelson, are a class of rotation-invariant linear operators that may be used to analyze local orientation patterns in imagery. The most common examples of such operators are directional derivatives of Gaussians and their 2-D Hilbert transforms. The inherent symmetry of these filters produces an orientation response that is periodic with period π, even when the underlying image structure does not have such symmetry. This problem may be alleviated by reconsidering the full class of steerable filters. In this paper, we develop a family of even- and odd- symmetric steerable filters that have a spatially asymmetric 'wedge-like' shape and are optimally localized in their orientation response. Unlike the original steerable filters, these filters are not based on directional derivatives and the Hilbert transform relationship is imposed on their angular components. We demonstrate the ability of these filters to properly represent oriented structures.
AB - Steerable filters, as developed by Freeman and Adelson, are a class of rotation-invariant linear operators that may be used to analyze local orientation patterns in imagery. The most common examples of such operators are directional derivatives of Gaussians and their 2-D Hilbert transforms. The inherent symmetry of these filters produces an orientation response that is periodic with period π, even when the underlying image structure does not have such symmetry. This problem may be alleviated by reconsidering the full class of steerable filters. In this paper, we develop a family of even- and odd- symmetric steerable filters that have a spatially asymmetric 'wedge-like' shape and are optimally localized in their orientation response. Unlike the original steerable filters, these filters are not based on directional derivatives and the Hilbert transform relationship is imposed on their angular components. We demonstrate the ability of these filters to properly represent oriented structures.
UR - http://www.scopus.com/inward/record.url?scp=0029234210&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029234210&partnerID=8YFLogxK
M3 - Conference contribution
SP - 189
EP - 194
BT - IEEE International Conference on Computer Vision
A2 - Anon, null
PB - IEEE
ER -