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Steerable random fields

: Roth, Stefan; Black, Michael


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 11th International Conference on Computer Vision, ICCV 2007 : 14-21 Oct. 2007, Rio de Janeiro, Brazil
Los Alamitos, Calif.: IEEE Computer Society, 2007
ISBN: 978-1-4244-1630-1
ISBN: 978-1-4244-1631-8
ISBN: 1-4244-1630-2
8 pp.
International Conference on Computer Vision (ICCV) <11, 2007, Rio de Janeiro>
Conference Paper
Fraunhofer IGD ()
computer vision; image data model; low-level image processing

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.