Roth, Stefan ; Black, Michael (2007)
Steerable Random Fields.
IEEE 11th International Conference on Computer Vision.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that are steered to the local image structure. In particular, we use the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure, and analyze the statistics of these steered filter responses in natural images. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random field models with anisotropic regularization and provides a statistical motivation for the latter. We demonstrate that steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2007 |
| Autor(en): | Roth, Stefan ; Black, Michael |
| Art des Eintrags: | Bibliographie |
| Titel: | Steerable Random Fields |
| Sprache: | Deutsch |
| Publikationsjahr: | 2007 |
| Verlag: | IEEE Computer Society, Los Alamitos, Calif. |
| Veranstaltungstitel: | IEEE 11th International Conference on Computer Vision |
| Kurzbeschreibung (Abstract): | In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that are steered to the local image structure. In particular, we use the structure tensor to obtain derivative responses that are either aligned with, or orthogonal to, the predominant local image structure, and analyze the statistics of these steered filter responses in natural images. Clique potentials are defined over steered filter responses using a Gaussian scale mixture model and are learned from training data. The SRF model connects random field models with anisotropic regularization and provides a statistical motivation for the latter. We demonstrate that steering the random field to the local image structure improves image denoising and inpainting performance compared with traditional pairwise MRFs. |
| Freie Schlagworte: | Forschungsgruppe Visual Inference (VINF), Computer vision, Image data models, Low level image processing |
| Fachbereich(e)/-gebiet(e): | nicht bekannt 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Apr 2018 09:03 |
| Letzte Änderung: | 16 Apr 2018 09:03 |
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