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ECCV
2008
Springer
15 years 20 days ago
Compressive Sensing for Background Subtraction
Abstract. Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reco...
Volkan Cevher, Aswin C. Sankaranarayanan, Marco F....
CVPR
2009
IEEE
14 years 5 months ago
Resolution-Invariant Image Representation and its applications
We present a Resolution-Invariant Image Representation (RIIR) framework in this paper. The RIIR framework includes the methods of building a set of multi-resolution bases from tra...
Jinjun Wang, Shenghuo Zhu, Yihong Gong
CVPR
2009
IEEE
1599views Computer Vision» more  CVPR 2009»
15 years 6 months ago
Multi-Label Sparse Coding for Automatic Image Annotation
In this paper, we present a multi-label sparse coding framework for feature extraction and classification within the context of automatic image annotation. First, each image is ...
Changhu Wang (University of Science and Technology...
ICCV
2009
IEEE
1318views Computer Vision» more  ICCV 2009»
15 years 3 months ago
Non-Local Sparse Models for Image Restoration
We propose in this paper to unify two different ap- proaches to image restoration: On the one hand, learning a basis set (dictionary) adapted to sparse signal descriptions has p...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
ALIFE
2006
13 years 10 months ago
The Infection Algorithm: An Artificial Epidemic Approach for Dense Stereo Correspondence
We present a new bio-inspired approach applied to a problem of stereo image matching. This approach is based on an artificial epidemic process, which we call the infection algorith...
Gustavo Olague, Francisco Fernández, Cynthi...