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» Online Selection of Tracking Features using AdaBoost
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ICASSP
2009
IEEE
15 years 4 months ago
Voronoi cell shaping for feature selection with discrete HMMs
In this paper, we introduce a novel vector quantization (VQ) scheme for distributing the quantization error equally among the quantized dimensions. Afterwards, the proposed VQ sch...
Joachim Schenk, Gerhard Rigoll
90
Voted
ETS
2011
IEEE
230views Hardware» more  ETS 2011»
13 years 9 months ago
Dynamic Test Set Selection Using Implication-Based On-Chip Diagnosis
—As circuits continue to scale to smaller feature sizes, wearout and latent defects are expected to cause an increasing number of errors in the field. Online error detection tec...
Nuno Alves, Y. Shi, N. Imbriglia, Jennifer Dworak,...
ISMAR
2006
IEEE
15 years 3 months ago
Online camera pose estimation in partially known and dynamic scenes
One of the key requirements of augmented reality systems is a robust real-time camera pose estimation. In this paper we present a robust approach, which does neither depend on of...
Gabriele Bleser, Harald Wuest, Didier Stricker
CVPR
2009
IEEE
16 years 4 months ago
Learning to Track with Multiple Observers
We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for...
Björn Stenger, Roberto Cipolla, Thomas Woodle...
82
Voted
ICCV
2007
IEEE
15 years 11 months ago
Probabilistic Color and Adaptive Multi-Feature Tracking with Dynamically Switched Priority Between Cues
We present a probabilistic multi-cue tracking approach constructed by employing a novel randomized template tracker and a constant color model based particle filter. Our approach ...
François Le Clerc, Lionel Oisel, Patrick P&...