Results 1  10
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9,658
Regularity Properties and Pathologies of PositionSpace renormalizationGroup transformations: Scope and Limitations of Gibbsian Theory
 J. Stat.Phys
, 1993
"... he pla t/9 21 ..."
Image denoising by sparse 3D transformdomain collaborative filtering
 IEEE TRANS. IMAGE PROCESS
, 2007
"... We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure d ..."
Abstract

Cited by 424 (32 self)
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We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2D image fragments (e.g., blocks) into 3D data arrays which we call “groups.” Collaborative filtering is a special procedure
Blobworld: Image segmentation using ExpectationMaximization and its application to image querying
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1999
"... Retrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation which provides a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture. This "B ..."
Abstract

Cited by 438 (10 self)
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;Blobworld" representation is created by clustering pixels in a joint colortextureposition feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection
LowTemperature Series for Renormalized Operators: the Ferromagnetic SquareLattice Ising Model
 J. Stat. Phys
, 1995
"... A method for computing lowtemperature series for renormalized operators in the twodimensional Ising model is proposed. Series for the renormalized magnetization and nearestneighbor correlation function are given for the majority rule transformation on 2 \Theta 2 blocks and random tiebreaker. ..."
Abstract

Cited by 10 (0 self)
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breaker. These series are applied to the study at very low temperature of the firstorder phase transition undergone by this model. We analyze how truncation in the renormalized Hamiltonian leads to spurious discontinuities of the Renormalization Group transformation. Keywords: Renormalization group, positionspace
Renormalization group flows from holography  Supersymmetry and a ctheorem
 ADV THEOR. MATH. PHYS
, 1999
"... We obtain first order equations that determine a supersymmetric kink solution in fivedimensional N = 8 gauged supergravity. The kink interpolates between an exterior antide Sitter region with maximal supersymmetry and an interior antide Sitter region with one quarter of the maximal supersymmetry. ..."
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Cited by 294 (25 self)
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. One eighth of supersymmetry is preserved by the kink as a whole. We interpret it as describing the renormalization group flow in N = 4 superYangMills theory broken to an N = 1 theory by the addition of a mass term for one of the three adjoint chiral superfields. A detailed correspondence is obtained
Segmentation using eigenvectors: A unifying view
 In ICCV
, 1999
"... Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are ..."
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Cited by 380 (1 self)
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. In many cases, this is done by associating with each pixel a feature vector (e.g. color, motion, texture, position) and using a clustering or grouping algorithm on these feature vectors. Perhaps the cleanest approach to segmenting points in feature space is based on mixture models in which one assumes
Renormalization Group Analysis of the SmallWorld Network Model
 Physics Letters A
"... We study the smallworld network model, which mimics the transition between regularlattice and randomlattice behavior in social networks of increasing size. We contend that the model displays a normal continuous phase transition with a divergent correlation length as the degree of randomness t ..."
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Cited by 140 (5 self)
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tends to zero. We propose a realspace renormalization group transformation for the model and demonstrate that the transformation is exact in the limit of large system size. We use this result to calculate the exact value of the single critical exponent for the system, and to derive the scaling form
Region Covariance: A Fast Descriptor for Detection And Classification
 In Proc. 9th European Conf. on Computer Vision
, 2006
"... We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance of dfeatures, e.g., the threedimensional color vector, the norm of first and second derivatives of intensity with respect to x and y, etc., characterizes a region of in ..."
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Cited by 278 (14 self)
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. Covariance matrices do not lie on Euclidean space, therefore we use a distance metric involving generalized eigenvalues which also follows from the Lie group structure of positive definite matrices. Feature matching is a simple nearest neighbor search under the distance metric and performed extremely
Renormalization Group
, 2009
"... The renormalization procedure in the last chapter has eliminated all UVdivergences from the Feynman integrals arising from large momenta in D = 4 − ε dimensions. This was necessary to obtain finite correlation functions in the limit ε → 0. We have seen in Chapter 7 that the dependence on the cutoff ..."
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on the cutoff or any other mass scale, introduced in the regularization process, changes the Ward identities derived from scale transformations by an additional term—the anomaly of scale invariance. The precise consequences of this term for the renormalized proper vertex functions were first investigated
Renormalization Group
, 2010
"... The renormalization procedure in the last chapter has eliminated all UVdivergences from the Feynman integrals arising from large momenta in D = 4 − ε dimensions. This was necessary to obtain finite correlation functions in the limit ε → 0. We have seen in Chapter 7 that the dependence on the cutoff ..."
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on the cutoff or any other mass scale, introduced in the regularization process, changes the Ward identities derived from scale transformations by an additional term—the anomaly of scale invariance. The precise consequences of this term for the renormalized proper vertex functions were first investigated
Results 1  10
of
9,658