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» Novel Observation Model for Probabilistic Object Tracking
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NIPS
2004
14 years 11 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
SIGMOD
2010
ACM
267views Database» more  SIGMOD 2010»
15 years 2 months ago
Processing proximity relations in road networks
Applications ranging from location-based services to multi-player online gaming require continuous query support to monitor, track, and detect events of interest among sets of mov...
Zhengdao Xu, Hans-Arno Jacobsen
CVPR
2009
IEEE
16 years 4 months ago
Illumination and Spatially Varying Specular Reflectance from a Single View
Estimating the illumination and the reflectance properties of an object surface from a sparse set of images is an important but inherently ill-posed problem. The problem becomes...
Kenji Hara (Kyushu University), Ko Nishino (Drexel...
ARC
2009
Springer
140views Hardware» more  ARC 2009»
15 years 4 months ago
FPGA-Based Anomalous Trajectory Detection Using SOFM
A system for automatically classifying the trajectory of a moving object in a scene as usual or suspicious is presented. The system uses an unsupervised neural network (Self Organi...
Kofi Appiah, Andrew Hunter, Tino Kluge, Philip Aik...
NIPS
1998
14 years 11 months ago
Facial Memory Is Kernel Density Estimation (Almost)
We compare the ability of three exemplar-based memory models, each using three different face stimulus representations, to account for the probability a human subject responded &q...
Matthew N. Dailey, Garrison W. Cottrell, Thomas A....