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TASLP
2010
78views more  TASLP 2010»
13 years 2 months ago
Solving Demodulation as an Optimization Problem
We introduce two new methods for the demodulation of acoustic signals by posing the problem in a convex optimization framework. This allows the parameters of the modulator and carr...
Gregory Sell, Malcolm Slaney
ACCV
2007
Springer
14 years 1 months ago
Color Constancy Via Convex Kernel Optimization
This paper introduces a novel convex kernel based method for color constancy computation with explicit illuminant parameter estimation. A simple linear render model is adopted and ...
Xiaotong Yuan, Stan Z. Li, Ran He
CVPR
2010
IEEE
13 years 10 months ago
Co-clustering of Image Segments Using Convex Optimization Applied to EM Neuronal Reconstruction
This paper addresses the problem of jointly clustering two segmentations of closely correlated images. We focus in particular on the application of reconstructing neuronal structu...
Shiv Vitaladevuni, Ronen Basri
NIPS
2008
13 years 9 months ago
Exact Convex Confidence-Weighted Learning
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that repr...
Koby Crammer, Mark Dredze, Fernando Pereira
JSCIC
2010
231views more  JSCIC 2010»
13 years 2 months ago
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction
Variational models for image segmentation have many applications, but can be slow to compute. Recently, globally convex segmentation models have been introduced which are very rel...
Tom Goldstein, Xavier Bresson, Stanley Osher