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A Dynamic Scale–Space Paradigm
Journal of Mathematical Imaging and Vision, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Salden, A.H. +2 more
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From Gaussian scale-space to B-spline scale-space
1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999The Gaussian kernel has long been used in the classical multiscale analysis. The purpose of the paper is to propose the uniform B-spline as an alternative for the visual modeling. A general framework for various scale-space representations is formulated using the B-spline approach.
Wang, Yu-Ping, Lee, S.L.
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International Journal of Computer Vision, 2003
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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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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
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Florack, Luc +3 more
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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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