Spectral Matting
Anat Levin, Alex Rav-Acha, Dani Lischinski
Abstract
We present 'spectral matting': a new approach to natural image
matting that automatically computes a set of fundamental fuzzy
matting components from the smallest eigenvectors of a
suitably defined Laplacian matrix. Thus, our approach extends
spectral segmentation techniques, whose goal is to extract hard
segments, to the extraction of soft matting components. These
components may then be used as building blocks to easily construct
semantically meaningful foreground mattes, either in an unsupervised
fashion, or based on a small amount of user input.
»A Technical Report with some more details (PDF)
Ground Truth data
(open the compressed file using: tar -xvvzf GT.tar.gz)Source Code
(open the compressed file using: tar -xvvzf code.tar.gz)
The spectral matting process:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|