Results 321 to 330 of about 15,521,488 (377)
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International Journal of Computer Vision, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '95, 1995
Big information worlds cause big problems for interfaces. There is too much to see. They are hard to navigate. An armada of techniques has been proposed to present the many scales of information needed. Space-scale diagrams provide an analytic framework for much of this work.
George W. Furnas, Benjamin B. Bederson
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Big information worlds cause big problems for interfaces. There is too much to see. They are hard to navigate. An armada of techniques has been proposed to present the many scales of information needed. Space-scale diagrams provide an analytic framework for much of this work.
George W. Furnas, Benjamin B. Bederson
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Pattern Recognition, 1997
In this paper, we first approximate the Gaussian function with any scale by the linear finite combination of Gaussian functions with dyadic scale; consequently, the scale space can be constructed much more efficiently: we only perform smoothing at these dyadic scales and the smoothed signals at other scales can be found by calculating linear ...
Ge Cong, Song De Ma
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In this paper, we first approximate the Gaussian function with any scale by the linear finite combination of Gaussian functions with dyadic scale; consequently, the scale space can be constructed much more efficiently: we only perform smoothing at these dyadic scales and the smoothed signals at other scales can be found by calculating linear ...
Ge Cong, Song De Ma
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Laplacian Scale-Space Behavior of Planar Curve Corners
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Scale-space behavior of corners is important for developing an efficient corner detection algorithm. In this paper, we analyze the scale-space behavior with the Laplacian of Gaussian (LoG) operator on a planar curve which constructs Laplacian Scale Space
Xiaohong Zhang +4 more
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WIREs Computational Statistics, 2010
AbstractWe discuss methods that use multiscale smoothing for explorative data analysis and inference. The problems considered involve nonparametric density estimation and regression, time series analysis, image analysis, and more general spatial data analysis settings.
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AbstractWe discuss methods that use multiscale smoothing for explorative data analysis and inference. The problems considered involve nonparametric density estimation and regression, time series analysis, image analysis, and more general spatial data analysis settings.
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Proceedings of 13th International Conference on Pattern Recognition, 1996
We approximate Gaussian function with any scale by linear combination of Gaussian functions with dyadic scales so that scale space can be constructed much more efficiently. The approximation error is so small that our approach can be used widely in computer vision and pattern recognition.
null Ge Cong, null SongDe Ma
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We approximate Gaussian function with any scale by linear combination of Gaussian functions with dyadic scales so that scale space can be constructed much more efficiently. The approximation error is so small that our approach can be used widely in computer vision and pattern recognition.
null Ge Cong, null SongDe Ma
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Journal of Mathematical Imaging and Vision, 1994
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Florack, Luc +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Florack, Luc +3 more
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1992
We introduce a novel notion of scale for signals which we illustrate for shape. It is based on the notion of entropy and a view of shocks as “black holes of information”. We propose that to properly place features in a hierarchy, we need both linear, global, and instantaneously propagated smoothing (e.g.
Benjamin B. Kimia +2 more
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We introduce a novel notion of scale for signals which we illustrate for shape. It is based on the notion of entropy and a view of shocks as “black holes of information”. We propose that to properly place features in a hierarchy, we need both linear, global, and instantaneously propagated smoothing (e.g.
Benjamin B. Kimia +2 more
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2002
Scale spaces allow us to organize, compare and analyse differently sized structures of an object. In this work, we present and compare five ways of discretizing the Gaussian scale-space: sampling Gaussian distributions; recursively calculating Gaussian approximations; using Splines; approximating by first-order generators; and finally, by a new method ...
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Scale spaces allow us to organize, compare and analyse differently sized structures of an object. In this work, we present and compare five ways of discretizing the Gaussian scale-space: sampling Gaussian distributions; recursively calculating Gaussian approximations; using Splines; approximating by first-order generators; and finally, by a new method ...
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SIGNAL MATCHING THROUGH SCALE SPACE
International Journal of Computer Vision, 1987Given a collection of similar signals that have been deformed with respect to each other, the general signal-matching problem is to recover the deformation. We formulate the problem as the minimization of an energy measure that combines a smoothness term and a similarity term.
Andrew Witkin +2 more
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