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» An Oscillatory Neural Model of Multiple Object Tracking
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NIPS
2008
13 years 10 months ago
Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM
Neural activity is non-stationary and varies across time. Hidden Markov Models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. W...
Kentaro Katahira, Jun Nishikawa, Kazuo Okanoya, Ma...
CVPR
2009
IEEE
15 years 4 months ago
Visual Tracking with Online Multiple Instance Learning
In this paper, we address the problem of learning an adaptive appearance model for object tracking. In particular, a class of tracking techniques called “tracking by detection...
Boris Babenko, Ming-Hsuan Yang, Serge J. Belongie
ICIP
2003
IEEE
14 years 2 months ago
A flexible multimodal object tracking system
In this paper we present a flexible multimodal object tracking system. It is based on a particle filter which combines the outputs of different measurement methods (also called ...
Harald Breit, Gerhard Rigoll
IJCV
2007
118views more  IJCV 2007»
13 years 8 months ago
Disambiguating Visual Motion by Form-Motion Interaction - a Computational Model
The neural mechanisms underlying motion segregation and integration still remain unclear to a large extent. Local motion estimates often are ambiguous in the lack of form features,...
Pierre Bayerl, Heiko Neumann
VIP
2003
13 years 10 months ago
Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces
The Continuously Adaptive Mean Shift Algorithm (CamShift) is an adaptation of the Mean Shift algorithm for object tracking that is intended as a step towards head and face trackin...
John G. Allen, Richard Y. D. Xu, Jesse S. Jin