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» An algorithmic framework for network reconstruction
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CVPR
2009
IEEE
1382views Computer Vision» more  CVPR 2009»
16 years 11 months ago
Super-Resolution via Recapture and Bayesian Effect Modeling
This paper presents Bayesian edge inference (BEI), a single-frame super-resolution method explicitly grounded in Bayesian inference that addresses issues common to existing meth...
Bryan S. Morse, Dan Ventura, Kevin D. Seppi, Neil ...
ICIP
2004
IEEE
16 years 5 months ago
Rate-distortion optimized streaming for 3-D wavelet video
We propose a rate-distortion optimized framework to stream scalable bitstreams of 3-D wavelet video stored at the sender to a remote receiver. Based on the source rate-distortion ...
Chuo-Ling Chang, Sangeun Han, Bernd Girod
123
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ICML
2009
IEEE
16 years 4 months ago
Discriminative k-metrics
The k q-flats algorithm is a generalization of the popular k-means algorithm where q dimensional best fit affine sets replace centroids as the cluster prototypes. In this work, a ...
Arthur Szlam, Guillermo Sapiro
CISS
2008
IEEE
15 years 5 months ago
Network tomography based on additive metrics
Inference of the network structure (e.g., routing topology) and dynamics (e.g., traffic matrices, link performance) is an important component in many network design and management ...
Jian Ni, Sekhar Tatikonda
TNN
1998
123views more  TNN 1998»
15 years 3 months ago
A general framework for adaptive processing of data structures
—A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to r...
Paolo Frasconi, Marco Gori, Alessandro Sperduti