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2007
Conference Paper
Title

Steerable random fields

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.
Author(s)
Roth, Stefan
TU Darmstadt GRIS
Black, Michael
Brown University
Mainwork
IEEE 11th International Conference on Computer Vision, ICCV 2007  
Conference
International Conference on Computer Vision (ICCV) 2007  
DOI
10.1109/ICCV.2007.4408981
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • computer vision

  • image data model

  • low-level image processing

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