Spectral Matting

Author

Levin, Anat ; Rav-Acha, Alex ; Lischinski, Dani

Author_Institution

MIT, Cambridge, MA

Volume

30

Issue

10

fYear

2008

Firstpage

1699

Lastpage

1712

Abstract

We present spectral matting: a new approach to natural image matting that automatically computes a basis set of 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.

Keywords

Laplace equations; eigenvalues and eigenfunctions; feature extraction; fuzzy set theory; image segmentation; matrix algebra; Laplacian matrix; eigenvectors; fuzzy matting components; natural image matting; spectral matting; spectral segmentation techniques; image segmentation; matting; spectral analysis; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/TPAMI.2008.168

Filename

4547428