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APPROX
2009
Springer
96views Algorithms» more  APPROX 2009»
15 years 4 months ago
On the Optimality of Gluing over Scales
We show that for every α > 0, there exist n-point metric spaces (X, d) where every “scale” admits a Euclidean embedding with distortion at most α, but the whole space req...
Alexander Jaffe, James R. Lee, Mohammad Moharrami
ICPR
2004
IEEE
15 years 10 months ago
Learning Spatial Context from Tracking using Penalised Likelihoods
MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientat...
Hammadi Nait-Charif, Stephen J. McKenna
SCALESPACE
1997
Springer
15 years 1 months ago
From High Energy Physics to Low Level Vision
A geometric framework for image scale space, enhancement, and segmentation is presented. We consider intensity images as surfaces in the (x I) space. The image is thereby a 2D surf...
Ron Kimmel, Nir A. Sochen, Ravi Malladi
ECCV
2010
Springer
15 years 2 months ago
Seeing through Obscure Glass
Obscure glass is textured glass designed to separate spaces and “obscure” visibility between the spaces. Such glass is used to provide privacy while still allowing light to fl...
ICML
2003
IEEE
15 years 10 months ago
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
Zhihua Zhang