Add to ORKGThis unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represe
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Sparse representations account for most or all of the information of a signal by a linear combinatio...
Sparse representation of signals has recently emerged as a major research area. It is well-known tha...
International audienceIn recent years, a large amount of multi-disciplinary research has been conduc...
This technical report combines two commonly-themed submissions to ICCV 2007. The two papers reconsid...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
Techniques from sparse signal representation are beginning to see significant impact in computer vis...
Sparse representation has been well investigated and discussed over the past decade due to its abili...
The works presented in this thesis focus on sparsity in the real world signals, its applications in ...
In the present-day scenario, there are various methods to process and represent a signal according t...
Abstract Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such t...
International audienceSparse representation has attracted much attention from researchers in fields ...
This dissertation describes a novel selection-based dictionary learning method with a sparse represe...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Natural images have the intrinsic property that they can be sparsely represented as a linear combina...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Sparse representations account for most or all of the information of a signal by a linear combinatio...
Sparse representation of signals has recently emerged as a major research area. It is well-known tha...
International audienceIn recent years, a large amount of multi-disciplinary research has been conduc...
This technical report combines two commonly-themed submissions to ICCV 2007. The two papers reconsid...
Arguably one of the most notable forms of the principle of parsimony was formulated by the philosoph...
Techniques from sparse signal representation are beginning to see significant impact in computer vis...
Sparse representation has been well investigated and discussed over the past decade due to its abili...
The works presented in this thesis focus on sparsity in the real world signals, its applications in ...
In the present-day scenario, there are various methods to process and represent a signal according t...
Abstract Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such t...
International audienceSparse representation has attracted much attention from researchers in fields ...
This dissertation describes a novel selection-based dictionary learning method with a sparse represe...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
Natural images have the intrinsic property that they can be sparsely represented as a linear combina...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Sparse representations account for most or all of the information of a signal by a linear combinatio...
Sparse representation of signals has recently emerged as a major research area. It is well-known tha...