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gradient maps
"... In this paper we propose an extension for the algorithms of imagetogeometry registration by Mutual Information(MI) to improve the performance and the quality of the alignment. Proposed for the registration of multi modal medical images, in the last years MI has been adapted to align a 3D model to ..."
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of the acquisition environment; the characteristics of the image background, especially non uniform background, that can degrade the convergence of the registration. To improve the quality of the registration in these cases we propose to compute the MI between the gradient map of the 3D rendering and the gradient
CONVEXITY PROPERTIES OF GRADIENT MAPS
, 2007
"... We consider the action of a real reductive group G on a Kähler manifold Z which is the restriction of a holomorphic action of the complexified group G C. We assume that the induced action of a compatible maximal compact subgroup U of G C on Z is Hamiltonian. We have an associated gradient map µp: Z ..."
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Cited by 4 (2 self)
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We consider the action of a real reductive group G on a Kähler manifold Z which is the restriction of a holomorphic action of the complexified group G C. We assume that the induced action of a compatible maximal compact subgroup U of G C on Z is Hamiltonian. We have an associated gradient map µp
Snakes, Shapes, and Gradient Vector Flow
 IEEE TRANSACTIONS ON IMAGE PROCESSING
, 1998
"... Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new extern ..."
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Cited by 743 (16 self)
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external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a graylevel or binary edge map derived from the image. It differs fundamentally from traditional snake external forces
Mean shift, mode seeking, and clustering
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeking proce ..."
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Cited by 620 (0 self)
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seeking process on a surface constructed with a “shadow ” kernel. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis is treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications
Quasiregular Gradient Mappings and Strong Solutions of Elliptic Equations
 CONTEMPORARY MATHEMATICS
"... We prove that quasiregular gradient mappings exhibit higher degree of Hölder continuity than the one that is optimal for general quasiregular mappings. This improves a classical result of Morrey on the regularity of strong solutions of uniformly elliptic PDEs with measurable coefficients. Our Hölde ..."
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Cited by 3 (2 self)
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We prove that quasiregular gradient mappings exhibit higher degree of Hölder continuity than the one that is optimal for general quasiregular mappings. This improves a classical result of Morrey on the regularity of strong solutions of uniformly elliptic PDEs with measurable coefficients. Our
CLASSIFICATION OF HOMOTOPY CLASSES OF EQUIVARIANT GRADIENT MAPS
"... Abstract. Let V be an orthogonal representation of a compact Lie group G and let S(V), D(V) be the unit sphere and disc of V, respectively. If F: V → R is a Ginvariant C1map then the Gequivariant gradient C0map ∇F: V → V is said to be admissible provided that (∇F)−1(0) ∩ S(V) = ∅. We classify ..."
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Abstract. Let V be an orthogonal representation of a compact Lie group G and let S(V), D(V) be the unit sphere and disc of V, respectively. If F: V → R is a Ginvariant C1map then the Gequivariant gradient C0map ∇F: V → V is said to be admissible provided that (∇F)−1(0) ∩ S(V) = ∅. We
Gradientmappingbased method for video enhancement
"... Abstract: In order to improve the visual effect of nighttime videos, an effective video enhancement method based on gradient mapping is proposed. The proposed method firstly is to change color space from RGB to HSI, secondly the horizontal gradient and vertical gradient components of frames are calc ..."
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Abstract: In order to improve the visual effect of nighttime videos, an effective video enhancement method based on gradient mapping is proposed. The proposed method firstly is to change color space from RGB to HSI, secondly the horizontal gradient and vertical gradient components of frames
Learning LongTerm Dependencies with Gradient Descent is Difficult
 TO APPEAR IN THE SPECIAL ISSUE ON RECURRENT NETWORKS OF THE IEEE TRANSACTIONS ON NEURAL NETWORKS
"... Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in th ..."
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Cited by 374 (35 self)
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Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present
A Jacobian inequality for gradient maps on the sphere and its application to directional statistics
 Communications in Statistics – Theory and Methods, Preprint: arXiv: 0906.0874
"... In the field of optimal transport theory, an optimal map is known to be a gradient map of a potential function satisfying costconvexity. In this paper, the Jacobian determinant of a gradient map is shown to be logconcave with respect to a convex combination of the potential functions when the unde ..."
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Cited by 9 (0 self)
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In the field of optimal transport theory, an optimal map is known to be a gradient map of a potential function satisfying costconvexity. In this paper, the Jacobian determinant of a gradient map is shown to be logconcave with respect to a convex combination of the potential functions when
GRADIENT MAP OF ISOPARAMETRIC POLYNOMIAL AND ITS APPLICATION TO GINZBURGLANDAU SYSTEM
, 906
"... Abstract. In this note, we study properties of the gradient map of the isoparametric polynomial. For a given isoparametric hypersurface in sphere, we calculate explicitly the gradient map of its isoparametric polynomial which turns out many interesting phenomenons and applications. We find that it s ..."
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Cited by 5 (3 self)
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Abstract. In this note, we study properties of the gradient map of the isoparametric polynomial. For a given isoparametric hypersurface in sphere, we calculate explicitly the gradient map of its isoparametric polynomial which turns out many interesting phenomenons and applications. We find
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