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» Maximum-Likelihood Image Matching
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PAMI
2012
13 years 2 days ago
Color Constancy with Spatio-Spectral Statistics
—We introduce an efficient maximum likelihood approach for one part of the color constancy problem: removing from an image the color cast caused by the spectral distribution of ...
Ayan Chakrabarti, Keigo Hirakawa, Todd Zickler
66
Voted
ICIAP
2007
ACM
15 years 9 months ago
Sparseness Achievement in Hidden Markov Models
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irreleva...
Manuele Bicego, Marco Cristani, Vittorio Murino
83
Voted
TIP
2010
129views more  TIP 2010»
14 years 4 months ago
Image Segmentation by MAP-ML Estimations
Abstract--Image segmentation plays an important role in computer vision and image analysis. In this paper, image segmentation is formulated as a labeling problem under a probabilit...
Shifeng Chen, Liangliang Cao, Yueming Wang, Jianzh...
HUMO
2000
Springer
15 years 1 months ago
Specialized Mappings and the Estimation of Human Body Pose from a Single Image
We present an approach for recovering articulated body pose from single monocular images using the Specialized Mappings Architecture (SMA), a non-linear supervised learning archit...
Rómer Rosales, Stan Sclaroff
CSDA
2007
94views more  CSDA 2007»
14 years 9 months ago
Some extensions of score matching
Many probabilistic models are only defined up to a normalization constant. This makes maximum likelihood estimation of the model parameters very difficult. Typically, one then h...
Aapo Hyvärinen