DocumentCode
2919568
Title
Nonlocal matting
Author
Lee, Philip ; Wu, Ying
Author_Institution
Northwestern Univ., Evanston, IL, USA
fYear
2011
fDate
20-25 June 2011
Firstpage
2193
Lastpage
2200
Abstract
This work attempts to considerably reduce the amount of user effort in the natural image matting problem. The key observation is that the nonlocal principle, introduced to denoise images, can be successfully applied to the alpha matte to obtain sparsity in matte representation, and therefore dramatically reduce the number of pixels a user needs to manually label. We show how to avoid making the user provide redundant and unnecessary input, develop a method for clustering the image pixels for the user to label, and a method to perform high-quality matte extraction. We show that this algorithm is therefore faster, easier, and higher quality than state of the art methods.
Keywords
feature extraction; image denoising; image representation; pattern clustering; high-quality matte extraction; image denoising; image pixel clustering; matte representation; natural image matting problem; nonlocal matting; nonlocal principle; pixel reduction; Accuracy; Cameras; Clustering algorithms; Humans; Image color analysis; Kernel; Laplace equations;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on
Conference_Location
Providence, RI
ISSN
1063-6919
Print_ISBN
978-1-4577-0394-2
Type
conf
DOI
10.1109/CVPR.2011.5995665
Filename
5995665
Link To Document