The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
V Cappellini - One of the best experts on this subject based on the ideXlab platform.
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a quasi Euclidean Norm to speed up
2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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A quasi-Euclidean Norm to speed up vector median filtering
IEEE Transactions on Image Processing, 2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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ICIP - Optimum linear approximation of the Euclidean Norm to speed up vector median filtering
IEEE Transactions on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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Optimum linear approximation of the Euclidean Norm to speed up vector median filtering
Proceedings. International Conference on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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fast vector median filter based on Euclidean Norm approximation
IEEE Signal Processing Letters, 1994Co-Authors: Mauro Barni, V Cappellini, A MecocciAbstract:The vector median filter has good filtering capabilities; nevertheless, its huge computational complexity significantly limits its practical usability. A vector median filter based on a fast approximation of the Euclidean Norm is presented. The proposed algorithm couples computational and filtering effectiveness, and it is well suited for hardware implementation. Theoretical and experimental results regarding both approximation error and speed improvement prove the validity of the proposed algorithm. >
Mauro Barni - One of the best experts on this subject based on the ideXlab platform.
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a quasi Euclidean Norm to speed up
2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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A quasi-Euclidean Norm to speed up vector median filtering
IEEE Transactions on Image Processing, 2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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ICIP - Optimum linear approximation of the Euclidean Norm to speed up vector median filtering
IEEE Transactions on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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Optimum linear approximation of the Euclidean Norm to speed up vector median filtering
Proceedings. International Conference on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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fast vector median filter based on Euclidean Norm approximation
IEEE Signal Processing Letters, 1994Co-Authors: Mauro Barni, V Cappellini, A MecocciAbstract:The vector median filter has good filtering capabilities; nevertheless, its huge computational complexity significantly limits its practical usability. A vector median filter based on a fast approximation of the Euclidean Norm is presented. The proposed algorithm couples computational and filtering effectiveness, and it is well suited for hardware implementation. Theoretical and experimental results regarding both approximation error and speed improvement prove the validity of the proposed algorithm. >
Luis Rademacher - One of the best experts on this subject based on the ideXlab platform.
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the minimum Euclidean Norm point in a convex polytope wolfe s combinatorial algorithm is exponential
SIAM Journal on Computing, 2020Co-Authors: Jesus A De Loera, Jamie Haddock, Luis RademacherAbstract:The complexity of Philip Wolfe's method for the minimum Euclidean-Norm point problem over a convex polytope has remained unknown since he proposed the method in 1974. The method is important becaus...
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the minimum Euclidean Norm point in a convex polytope wolfe s combinatorial algorithm is exponential
Symposium on the Theory of Computing, 2018Co-Authors: Jesus A De Loera, Jamie Haddock, Luis RademacherAbstract:The complexity of Philip Wolfe’s method for the minimum Euclidean-Norm point problem over a convex polytope has remained unknown since he proposed the method in 1974. We present the first example that Wolfe’s method takes exponential time. Additionally, we improve previous results to show that linear programming reduces in strongly-polynomial time to the minimum Norm point problem over a simplex
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STOC - The minimum Euclidean-Norm point in a convex polytope: Wolfe's combinatorial algorithm is exponential
Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing - STOC 2018, 2018Co-Authors: Jesus A De Loera, Jamie Haddock, Luis RademacherAbstract:The complexity of Philip Wolfe’s method for the minimum Euclidean-Norm point problem over a convex polytope has remained unknown since he proposed the method in 1974. We present the first example that Wolfe’s method takes exponential time. Additionally, we improve previous results to show that linear programming reduces in strongly-polynomial time to the minimum Norm point problem over a simplex
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the minimum Euclidean Norm point on a convex polytope wolfe s combinatorial algorithm is exponential
arXiv: Optimization and Control, 2017Co-Authors: Jesus A De Loera, Jamie Haddock, Luis RademacherAbstract:The complexity of Philip Wolfe's method for the minimum Euclidean-Norm point problem over a convex polytope has remained unknown since he proposed the method in 1974. The method is important because it is used as a subroutine for one of the most practical algorithms for submodular function minimization. We present the first example that Wolfe's method takes exponential time. Additionally, we improve previous results to show that linear programming reduces in strongly-polynomial time to the minimum Norm point problem over a simplex.
Jaakko Makinen - One of the best experts on this subject based on the ideXlab platform.
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a bound for the Euclidean Norm of the difference between the best linear unbiased estimator and a linear unbiased estimator
Journal of Geodesy, 2002Co-Authors: Jaakko MakinenAbstract:A bound is established for the Euclidean Norm of the difference between the best linear unbiased estimator and any linear unbiased estimator in the general linear model. The bound involves the spectral Norm of the difference between the dispersion matrices of the two estimators, and the residual sum of squares, all evaluated at the assumed model, but is independent of the provenance of the observation vector at hand. The bound, a straightforward consequence of first principles in Gauss–Markov theory, generalizes previous results on the difference between the best linear unbiased estimator and the ordinary least-squares estimator. In a numerical example from repeated precise levelling, the bound is used to analyse the sensitivity of estimates of vertical motion to the choice of estimator.
Franco Bartolini - One of the best experts on this subject based on the ideXlab platform.
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a quasi Euclidean Norm to speed up
2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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A quasi-Euclidean Norm to speed up vector median filtering
IEEE Transactions on Image Processing, 2000Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector fields obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. Euclidean distance is evaluated by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. In this paper an optimal piece-wise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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ICIP - Optimum linear approximation of the Euclidean Norm to speed up vector median filtering
IEEE Transactions on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.
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Optimum linear approximation of the Euclidean Norm to speed up vector median filtering
Proceedings. International Conference on Image Processing, 1995Co-Authors: Mauro Barni, Fabio Buti, Franco Bartolini, V CappelliniAbstract:For reducing impulsive noise without degrading image contours, median filtering is a powerful tool. In multiband images, as for example color images or vector field obtained by optic flow computation, a vector median filter can be used. Vector median filters are defined on the basis of a suitable distance, the best performing distance being the Euclidean. The Euclidean distance is computed by using the Euclidean Norm which is quite demanding from the point of view of computation given that a square root is required. An optimal piecewise linear approximation of the Euclidean Norm is presented which is applied to vector median filtering.