{"date_created":"2018-12-11T12:01:59Z","title":"A global perspective on MAP inference for low level vision","month":"05","doi":"10.1109/ICCV.2009.5459434","date_updated":"2021-01-12T07:41:46Z","citation":{"short":"O. Woodford, C. Rother, V. Kolmogorov, in:, IEEE, 2009, pp. 2319–2326.","ama":"Woodford O, Rother C, Kolmogorov V. A global perspective on MAP inference for low level vision. In: IEEE; 2009:2319-2326. doi:<a href=\"https://doi.org/10.1109/ICCV.2009.5459434\">10.1109/ICCV.2009.5459434</a>","ieee":"O. Woodford, C. Rother, and V. Kolmogorov, “A global perspective on MAP inference for low level vision,” presented at the ICCV: International Conference on Computer Vision, 2009, pp. 2319–2326.","apa":"Woodford, O., Rother, C., &#38; Kolmogorov, V. (2009). A global perspective on MAP inference for low level vision (pp. 2319–2326). Presented at the ICCV: International Conference on Computer Vision, IEEE. <a href=\"https://doi.org/10.1109/ICCV.2009.5459434\">https://doi.org/10.1109/ICCV.2009.5459434</a>","chicago":"Woodford, Oliver, Carsten Rother, and Vladimir Kolmogorov. “A Global Perspective on MAP Inference for Low Level Vision,” 2319–26. IEEE, 2009. <a href=\"https://doi.org/10.1109/ICCV.2009.5459434\">https://doi.org/10.1109/ICCV.2009.5459434</a>.","mla":"Woodford, Oliver, et al. <i>A Global Perspective on MAP Inference for Low Level Vision</i>. IEEE, 2009, pp. 2319–26, doi:<a href=\"https://doi.org/10.1109/ICCV.2009.5459434\">10.1109/ICCV.2009.5459434</a>.","ista":"Woodford O, Rother C, Kolmogorov V. 2009. A global perspective on MAP inference for low level vision. ICCV: International Conference on Computer Vision, 2319–2326."},"conference":{"name":"ICCV: International Conference on Computer Vision"},"extern":1,"quality_controlled":0,"day":"01","abstract":[{"lang":"eng","text":"In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, MRFs inherently encourage delta function marginal statistics. By contrast, many low-level vision problems have heavy tailed marginal statistics, making the MRF model unsuitable. In this paper we introduce a more general Marginal Probability Field (MPF), of which the MRF is a special, linear case, and show that convex energy MPFs can be used to encourage arbitrary marginal statistics. We introduce a flexible, extensible framework for effectively optimizing the resulting NP-hard MAP problem, based around dual-decomposition and a modified mincost flow algorithm, and which achieves global optimality in some instances. We use a range of applications, including image denoising and texture synthesis, to demonstrate the benefits of this class of MPF over MRFs."}],"status":"public","author":[{"last_name":"Woodford","full_name":"Woodford, Oliver J","first_name":"Oliver"},{"last_name":"Rother","full_name":"Rother, Carsten","first_name":"Carsten"},{"id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","first_name":"Vladimir","full_name":"Vladimir Kolmogorov","last_name":"Kolmogorov"}],"year":"2009","_id":"3203","page":"2319 - 2326","publication_status":"published","publisher":"IEEE","date_published":"2009-05-01T00:00:00Z","type":"conference","publist_id":"3481"}