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Proceedings Paper

Automatic framework for highly efficient natural image matting
Author(s): Fazhi He; Yue Wu; Dengyi Zhang; Zhiyong Huang; Lingyun Wei; Chunxia Xiao
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Paper Abstract

Traditional image matting approaches requires user interaction. This paper proposes an automatic framework for natural image matting. The method seamlessly incorporates image matting with the top-down process of segmentation by weighted aggregation to get a rich and multi-scale grapy pyramid representation of the input image. Using the coupling between aggregates in the graph pyramid, the region for matting is detected adaptively and automatically. Meanwhile, foreground and background regions are determined with state variables. An energy function is constructed to represent the similarity and smoothness properties of a matte and is iteratively optimized. Under the automatic matting framework, color sampling is more accurate than existing methods since multi-scale measurements such as intensity and texture are fully considered. Experiments show that the proposed automatic method is more efficient to extract high quality matte even for difficult images in which foreground and background have very similar colors. Another attractive feature of the method is that it can extract mattes for multi-objects at one computing time.

Paper Details

Date Published: 15 November 2007
PDF: 9 pages
Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67872D (15 November 2007); doi: 10.1117/12.752763
Show Author Affiliations
Fazhi He, Wuhan Univ. (China)
Yue Wu, Wuhan Univ. (China)
Dengyi Zhang, Wuhan Univ. (China)
Zhiyong Huang, Wuhan Univ. (China)
Lingyun Wei, Wuhan Univ. (China)
Chunxia Xiao, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 6787:
MIPPR 2007: Multispectral Image Processing
Henri Maître; Hong Sun; Jianguo Liu; Enmin Song, Editor(s)

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