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» Learning to Track with Multiple Observers
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ISMAR
2005
IEEE
14 years 1 months ago
Adaptive Line Tracking with Multiple Hypotheses for Augmented Reality
We present a real-time model-based line tracking approach with adaptive learning of image edge features that can handle partial occlusion and illumination changes. A CAD (VRML) mo...
Harald Wuest, Florent Vial, Didier Stricker
AMDO
2010
Springer
13 years 5 months ago
Multiple-Activity Human Body Tracking in Unconstrained Environments
We propose a method for human full-body pose tracking from measurements of wearable inertial sensors. Since the data provided by such sensors is sparse, noisy and often ambiguous, ...
Loren Arthur Schwarz, Diana Mateus, Nassir Navab
PAMI
2011
13 years 2 months ago
Learning a Family of Detectors via Multiplicative Kernels
—Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and wit...
Quan Yuan, Ashwin Thangali, Vitaly Ablavsky, Stan ...
HRI
2006
ACM
14 years 1 months ago
FOCUS: a generalized method for object discovery for robots that observe and interact with humans
The essence of the signal-to-symbol problem consists of associating a symbolic description of an object (e.g., a chair) to a signal (e.g., an image) that captures the real object....
Manuela M. Veloso, Paul E. Rybski, Felix von Hunde...
CVPR
2012
IEEE
11 years 10 months ago
Batch mode Adaptive Multiple Instance Learning for computer vision tasks
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
Wen Li, Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu