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Color TV: Total Variation Methods for Restoration of Vector Valued Images
 IEEE Trans. Image Processing
, 1996
"... We propose a new definition of the total variation norm for vector valued functions which can be applied to restore color and other vector valued images. The new TV norm has the desirable properties of (i) not penalizing discontinuities (edges) in the image, (ii) rotationally invariant in the image ..."
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Cited by 158 (13 self)
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space, and (iii) reduces to the usual TV norm in the scalar case. Some numerical experiments on denoising simple color images in RGB color space are presented. 1 Introduction During gathering and transfer of image data some noise and blur is usually introduced into the image. Several reconstruction
Consistency of the group lasso and multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2007
"... We consider the leastsquare regression problem with regularization by a block 1norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1norm where all spaces have dimension one, where it ..."
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Cited by 274 (33 self)
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We consider the leastsquare regression problem with regularization by a block 1norm, i.e., a sum of Euclidean norms over spaces of dimensions larger than one. This problem, referred to as the group Lasso, extends the usual regularization by the 1norm where all spaces have dimension one, where
Fast and Robust MultiFrame SuperResolution
 IEEE Transactions on Image ProcessinG
, 2003
"... In the last two decades, many papers have been published, proposing a variety of methods for multi frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses th ..."
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Cited by 272 (37 self)
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In the last two decades, many papers have been published, proposing a variety of methods for multi frame resolution enhancement. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses
A Nonlinear PrimalDual Method For Total VariationBased Image Restoration
, 1995
"... . We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity jruj in the definition of the TVnorm before we apply a linearization technique such as Newton ..."
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Cited by 232 (22 self)
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. We present a new method for solving total variation (TV) minimization problems in image restoration. The main idea is to remove some of the singularity caused by the nondifferentiability of the quantity jruj in the definition of the TVnorm before we apply a linearization technique
A duality based approach for realtime tvl1 optical flow
 In Ann. Symp. German Association Patt. Recogn
, 2007
"... Abstract. Variational methods are among the most successful approaches to calculate the optical flow between two image frames. A particularly appealing formulation is based on total variation (TV) regularization and the robust L 1 norm in the data fidelity term. This formulation can preserve discont ..."
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Cited by 198 (15 self)
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Abstract. Variational methods are among the most successful approaches to calculate the optical flow between two image frames. A particularly appealing formulation is based on total variation (TV) regularization and the robust L 1 norm in the data fidelity term. This formulation can preserve
Structured variable selection with sparsityinducing norms
, 2011
"... We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsityinducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual ℓ1norm and the group ℓ1norm by allowing the subsets to ov ..."
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Cited by 187 (27 self)
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We consider the empirical risk minimization problem for linear supervised learning, with regularization by structured sparsityinducing norms. These are defined as sums of Euclidean norms on certain subsets of variables, extending the usual ℓ1norm and the group ℓ1norm by allowing the subsets
The probability of duplicate gene preservation by subfunctionalization.
 Genetics
, 2000
"... ABSTRACT It has often been argued that geneduplication events are most commonly followed by a mutational event that silences one member of the pair, while on rare occasions both members of the pair are preserved as one acquires a mutation with a beneficial function and the other retains the origin ..."
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Cited by 261 (2 self)
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are periods of time suggests that some type of positive selecbelieved to be initially redundant in function, it is comtion must be offsetting the high rate of production of monly thought that one member of the pair will usually null alleles, and this is supported by frequent observabecome silenced
BRIEF: Binary robust independent elementary features
, 2010
"... We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the ..."
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Cited by 208 (5 self)
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the Hamming distance, which is very efficient to compute, instead of the L2 norm as is usually done. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and USURF on standard benchmarks and show that it yields a similar or better recognition performance, while running in a
Total Variation Blind Deconvolution
, 1996
"... In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed in [11]. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images [11] as well as some blurring functions, e.g. motion b ..."
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Cited by 197 (15 self)
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In this paper, we present a blind deconvolution algorithm based on the total variational (TV) minimization method proposed in [11]. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images [11] as well as some blurring functions, e.g. motion
The Internet and social life
 Journal of ComputerMediated Communication 12 (2007) 1143–1168 ª 2007 International Communication Association1165
, 2004
"... ABSTRACT—Much of social life is experienced through mental processes that are not intended and about which one is fairly oblivious. These processes are automatically triggered by features of the immediate social environment, such as the group memberships of other people, the qualities of their beha ..."
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Cited by 143 (0 self)
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ties of their behavior, and features of social situations (e.g., norms, one’s relative power). Recent research has shown these nonconscious influences to extend beyond the perception and interpretation of the social world to the actual guidance, over extended time periods, of one’s important goal pursuits and social
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