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 :
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