Abstract
In this paper we analyze the convexity and the quasiconvexity of shape from focus/defocus and image restoration. We show that these problems are strictly quasiconvex for a family of Bregman's divergences, and in particular for least-squares. In addition to giving novel analytical insight to these problems, this study can be readily exploited to design algorithms: One can do away with global minimizers and obtain the same optimal solution by employing simple and efficient local methods. We experimentally validate this investigation by comparing two minimization algorithms: one based on a local method (gradient-flow) and another based on a global method (graph cuts). We show that both algorithms find the global optimum. Finally, we fully characterize defocus-invariant textures, a class of textures that do not allow depth recovery. We show how to decompose textures into defocus-invariant and defocus-varying components, and how this decomposition can be used to dramatically improve depth estimates. ©2007 IEEE.
Original language | English |
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Title of host publication | Proceedings of the IEEE International Conference on Computer Vision |
DOIs | |
Publication status | Published - 2007 |
Event | 2007 IEEE 11th International Conference on Computer Vision - Rio de Janeiro, Brazil Duration: 14 Oct 2007 → 21 Oct 2007 |
Conference
Conference | 2007 IEEE 11th International Conference on Computer Vision |
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Abbreviated title | ICCV |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 14/10/07 → 21/10/07 |