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» Introduction to Randomized Algorithms
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CVPR
2009
IEEE
17 years 2 days ago
Alphabet SOUP: A Framework for Approximate Energy Minimization
Many problems in computer vision can be modeled using conditional Markov random fields (CRF). Since finding the maximum a posteriori (MAP) solution in such models is NP-hard, mu...
Stephen Gould (Stanford University), Fernando Amat...
CVPR
2009
IEEE
17 years 2 days ago
Efficient Scale Space Auto-Context for Image Segmentation and Labeling
The Conditional Random Fields (CRF) model, using patch-based classification bound with context information, has recently been widely adopted for image segmentation/ labeling. In...
Jiayan Jiang (UCLA), Zhuowen Tu (UCLA)
ICCV
2009
IEEE
16 years 10 months ago
A Probabilistic Framework for Partial Intrinsic Symmetries in Geometric Data
In this paper, we present a novel algorithm for partial intrinsic symmetry detection in 3D geometry. Unlike previous work, our algorithm is based on a conceptually simple and st...
Ruxandra Lasowski, Art Tevs, Hans-Peter Seidel, Mi...
CVPR
2000
IEEE
16 years 7 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
ECCV
2008
Springer
16 years 6 months ago
Search Space Reduction for MRF Stereo
We present an algorithm to reduce per-pixel search ranges for Markov Random Fields-based stereo algorithms. Our algorithm is based on the intuitions that reliably matched pixels ne...
Liang Wang, Hailin Jin, Ruigang Yang