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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Junbin Gao |
| Copyright Year | 2011 |
| Abstract | Image matting refers to the problem of accurately extracting foreground objects in images and video. The most recent work by Levin, Lischinski and Weiss (2008) in natural image matting relies on the local smoothness assumptions on foreground and background colors on which a cost function is established. The closed-form solution has been derived based on certain degree of user inputs. In this paper, we present a framework of formulating new cost function from the manifold learning perspective based on the so-called Local Tangential Space Alignment algorithm (Zhang and Zha, 2004) where the local smoothness assumptions have been replaced by implicit manifold structure defined in local color spaces. We illustrate our new algorithm using the standard benchmark images and very comparable results have been obtained. |
| Starting Page | 614 |
| Ending Page | 619 |
| File Size | 612315 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457720062 |
| DOI | 10.1109/DICTA.2011.109 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-06 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Manifolds Manifold Learning Laplace equations Closed-form solutions Smoothing methods Image color analysis LTSA Image matting Vectors Image reconstruction |
| Content Type | Text |
| Resource Type | Article |
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