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» Multiple Kernels for Object Detection
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ICCV
2005
IEEE
14 years 2 months ago
Learning Hierarchical Models of Scenes, Objects, and Parts
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...
ICIP
2009
IEEE
13 years 6 months ago
Object tracking by bidirectional learning with feature selection
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by nonparametric...
Heng Wang, Xinwen Hou, Cheng-Lin Liu
CORR
2008
Springer
104views Education» more  CORR 2008»
13 years 8 months ago
A Variational Inference Framework for Soft-In-Soft-Out Detection in Multiple Access Channels
We propose a unified framework for deriving and studying soft-in soft-out (SISO) detection in multiple-access channels using the concept of variational inference. The proposed fram...
Darryl Dexu Lin, Teng Joon Lim
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
CVPR
2006
IEEE
14 years 2 months ago
Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection
Tracking of humans in videos is important for many applications. A major source of difficulty in performing this task is due to inter-human or scene occlusion. We present an appr...
Bo Wu, Ram Nevatia