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AAAI
1992
13 years 8 months ago
Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
It is often useful for a robot to construct a spatial representation of its environment from experiments and observations, in other words, to learn a map of its environment by exp...
Thomas Dean, Dana Angluin, Kenneth Basye, Sean P. ...
CVPR
1998
IEEE
14 years 9 months ago
Tracking People with Twists and Exponential Maps
This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. W...
Christoph Bregler, Jitendra Malik
ICCV
2003
IEEE
14 years 27 days ago
Computing MAP trajectories by representing, propagating and combining PDFs over groups
This paper addresses the problem of computing the trajectory of a camera from sparse positional measurements that have been obtained from visual localisation, and dense differenti...
Paul Smith, Tom Drummond, Kimon Roussopoulos
ISBI
2006
IEEE
14 years 8 months ago
Functional brain mapping with high-temporal resolution: introducing "evolutionary activation cells"
Functional image sequences obtained from image reconstruction techniques applied to Magneto and Electroencephalography (M/EEG) data convey a large amount of information in the spa...
Florence Gombert, Sylvain Baillet
CRV
2005
IEEE
103views Robotics» more  CRV 2005»
14 years 1 months ago
A Quantitative Comparison of 4 Algorithms for Recovering Dense Accurate Depth
: We report on 4 algorithms for recovering dense depth maps from long image sequences, where the camera motion is known a priori. All methods use a Kalman filter to integrate inte...
Baozhong Tian, John L. Barron