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CVPR
2009
IEEE
17 years 2 months ago
Learning to Track with Multiple Observers
We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for...
Björn Stenger, Roberto Cipolla, Thomas Woodle...
CVPR
2007
IEEE
16 years 9 months ago
Learning Kernel Expansions for Image Classification
Kernel machines (e.g. SVM, KLDA) have shown state-ofthe-art performance in several visual classification tasks. The classification performance of kernel machines greatly depends o...
Fernando De la Torre, Oriol Vinyals
ICPR
2010
IEEE
16 years 2 months ago
Gait Learning-Based Regenerative Model: A Level Set Approach
We propose a learning method for gait synthesis from a sequence of shapes(frames) with the ability to extrapolate to novel data. It involves the application of PCA, first to redu...
Muayed Sattar Al-Huseiny, Sasan Mahmoodi, Mark Nix...
ICARCV
2008
IEEE
184views Robotics» more  ICARCV 2008»
16 years 2 months ago
Learning informative point classes for the acquisition of object model maps
—This paper proposes a set of methods for building informative and robust feature point representations, used for accurately labeling points in a 3D point cloud, based on the typ...
Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow...
ICANNGA
2007
Springer
105views Algorithms» more  ICANNGA 2007»
16 years 1 months ago
Reinforcement Learning in Fine Time Discretization
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
Pawel Wawrzynski