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AAAI
1992
15 years 3 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
16 years 4 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
15 years 7 months 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
16 years 2 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»
15 years 7 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