Results 1 - 10 of 6,104

K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

by Michal Aharon, et al. , 2006
"... In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many and inc ..."
Abstract - Cited by 935 (41 self) - Add to MetaCart
In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinations of these atoms. Applications that use sparse representation are many

Stable recovery of sparse overcomplete representations in the presence of noise

by David L. Donoho, Michael Elad, Vladimir N. Temlyakov - IEEE TRANS. INFORM. THEORY , 2006
"... Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes t ..."
Abstract - Cited by 460 (22 self) - Add to MetaCart
Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes

Distributed compressed sensing of jointly sparse signals

by Marco F. Duarte, Shriram Sarvotham, Dror Baron, Michael B. Wakin, Richard G. Baraniuk - In Asilomar Conf. Signals, Sys., Comput , 2005
"... Abstract—Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed coding al ..."
Abstract - Cited by 80 (5 self) - Add to MetaCart
Abstract—Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we expand our theory for distributed compressed sensing (DCS) that enables new distributed coding

Multitask Learning,”

by Rich Caruana , Lorien Pratt , Sebastian Thrun , 1997
"... Abstract. Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for ..."
Abstract - Cited by 677 (6 self) - Add to MetaCart
Abstract. Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned

A computational approach to edge detection

by John Canny - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1986
"... This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumpti ..."
Abstract - Cited by 4675 (0 self) - Add to MetaCart
with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function

Recovery of jointly sparse signals from few random projections

by Michael B. Wakin, Marco F. Duarte, Shriram Sarvotham, Dror Baron, Richard G. Baraniuk , 2005
"... Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms ..."
Abstract - Cited by 29 (8 self) - Add to MetaCart
Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms

Measurement Bounds for Sparse Signal Ensembles via Graphical Models

by Marco F. Duarte, Michael B. Wakin, Dror Baron, Senior Member, Shriram Sarvotham, Richard G. Baraniuk
"... This paper is dedicated to the memory of Hyeokho Choi, our colleague, mentor, and friend. Abstract—In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework b ..."
Abstract - Cited by 8 (3 self) - Add to MetaCart
This paper is dedicated to the memory of Hyeokho Choi, our colleague, mentor, and friend. Abstract—In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework

Bounds on the Reconstruction of Sparse Signal Ensembles from Distributed Measurements

by Marco F. Duarte, Michael B. Wakin, Dror Baron, Shriram Sarvotham, Richard G. Baraniuk , 2011
"... This paper is dedicated to the memory of Hyeokho Choi, our colleague, mentor, and friend. In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework, allowing ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
This paper is dedicated to the memory of Hyeokho Choi, our colleague, mentor, and friend. In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework, allowing

GSAT and Dynamic Backtracking

by Matthew L. Ginsberg, David A. McAllester - Journal of Artificial Intelligence Research , 1994
"... There has been substantial recent interest in two new families of search techniques. One family consists of nonsystematic methods such as gsat; the other contains systematic approaches that use a polynomial amount of justification information to prune the search space. This paper introduces a new te ..."
Abstract - Cited by 386 (15 self) - Add to MetaCart
There has been substantial recent interest in two new families of search techniques. One family consists of nonsystematic methods such as gsat; the other contains systematic approaches that use a polynomial amount of justification information to prune the search space. This paper introduces a new

Distributed compressed sensing

by Dror Baron, Michael B. Wakin, Marco F. Duarte, Shriram Sarvotham, Richard G. Baraniuk , 2005
"... Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algori ..."
Abstract - Cited by 136 (26 self) - Add to MetaCart
Compressed sensing is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding
Results 1 - 10 of 6,104