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» Learning a Sparse Representation for Object Detection
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NIPS
2000
14 years 11 months ago
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show...
Xiaohui Xie, Richard H. R. Hahnloser, H. Sebastian...
95
Voted

Publication
1301views
15 years 9 months ago
Markovian Tracking-by-Detection from a Single, Uncalibrated Camera
We present an algorithm for multi-person tracking-bydetection in a particle filtering framework. To address the unreliability of current state-of-the-art object detectors, our a...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...
ECCV
2004
Springer
15 years 11 months ago
A Bayesian Framework for Multi-cue 3D Object Tracking
This paper presents a Bayesian framework for multi-cue 3D object tracking of deformable objects. The proposed spatio-temporal object representation involves a set of distinct linea...
Jan Giebel, Dariu Gavrila, Christoph Schnörr
100
Voted
BMVC
2010
14 years 7 months ago
Back to the Future: Learning Shape Models from 3D CAD Data
Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of o...
Michael Stark, Michael Goesele, Bernt Schiele
CRV
2011
IEEE
305views Robotics» more  CRV 2011»
13 years 9 months ago
Motion Segmentation by Learning Homography Matrices from Motor Signals
—Motion information is an important cue for a robot to separate foreground moving objects from the static background world. Based on the observation that the motion of the backgr...
Changhai Xu, Jingen Liu, Benjamin Kuipers