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ICCV
1999
IEEE
14 years 1 months ago
Learning Low-Level Vision
We describe a learning-based method for low-level vision problems--estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, m...
William T. Freeman, Egon C. Pasztor
ICCV
2009
IEEE
13 years 7 months ago
Real-time visual tracking via Incremental Covariance Tensor Learning
Visual tracking is a challenging problem, as an object may change its appearance due to pose variations, illumination changes, and occlusions. Many algorithms have been proposed t...
Yi Wu, Jian Cheng, Jinqiao Wang, Hanqing Lu
PAMI
2008
161views more  PAMI 2008»
13 years 9 months ago
TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models
The Expectation Maximization (EM) algorithm is widely used for learning finite mixture models despite its greedy nature. Most popular model-based clustering techniques might yield...
Chandan K. Reddy, Hsiao-Dong Chiang, Bala Rajaratn...

Book
519views
15 years 8 months ago
Information Theory, Inference, and Learning Algorithms
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and...
David J. C. MacKay
ICMCS
2005
IEEE
110views Multimedia» more  ICMCS 2005»
14 years 2 months ago
Learned color constancy from local correspondences
The ability of humans for color constancy, i.e. the ability to correct for color deviation caused by a different illumination, is far beyond computer vision performances: nowadays...
Tijmen Moerland, Frédéric Jurie