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TSP
2010
15 years 19 days ago
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimat...
Daniel Clark, Branko Ristic, Ba-Ngu Vo, Ba-Tuong V...

Book
778views
17 years 4 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
COMPGEOM
2009
ACM
16 years 14 days ago
Integral estimation from point cloud in d-dimensional space: a geometric view
Integration over a domain, such as a Euclidean space or a Riemannian manifold, is a fundamental problem across scientific fields. Many times, the underlying domain is only acces...
Chuanjiang Luo, Jian Sun, Yusu Wang
IPSN
2007
Springer
16 years 2 days ago
Object tracking in the presence of occlusions via a camera network
This paper describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication ban...
Ali Ozer Ercan, Abbas El Gamal, Leonidas J. Guibas
JMLR
2012
13 years 8 months ago
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria under a single theoretical inter...
Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luj...