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» Optimizing Parametric Total Variation Models
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SIAMIS
2008
279views more  SIAMIS 2008»
13 years 5 months ago
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
We propose, analyze and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorith...
Yilun Wang, Junfeng Yang, Wotao Yin, Yin Zhang
ICCV
2009
IEEE
14 years 10 months ago
Saliency Driven Total Variation Segmentation
This paper introduces an unsupervised color segmentation method. The underlying idea is to segment the input image several times, each time focussing on a different salient part...
Michael Donoser, Martin Urschler, Martin Hirzer an...
CVPR
2008
IEEE
14 years 7 months ago
An efficient algorithm for compressed MR imaging using total variation and wavelets
Compressed sensing, an emerging multidisciplinary field involving mathematics, probability, optimization, and signal processing, focuses on reconstructing an unknown signal from a...
Shiqian Ma, Wotao Yin, Yin Zhang, Amit Chakraborty
PROMISE
2010
13 years 8 days ago
Case-based reasoning vs parametric models for software quality optimization
Background: There are many data mining methods but few comparisons between them. For example, there are at least two ways to build quality optimizers, programs that find project o...
Adam Brady, Tim Menzies
ECCV
2004
Springer
14 years 7 months ago
A Robust Probabilistic Estimation Framework for Parametric Image Models
Models of spatial variation in images are central to a large number of low-level computer vision problems including segmentation, registration, and 3D structure detection. Often, i...
Maneesh Kumar Singh, Himanshu Arora, Narendra Ahuj...