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» Finding Paths in Video Sequences
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ICRA
2010
IEEE
142views Robotics» more  ICRA 2010»
13 years 8 months ago
Learning and planning high-dimensional physical trajectories via structured Lagrangians
— We consider the problem of finding sufficiently simple models of high-dimensional physical systems that are consistent with observed trajectories, and using these models to s...
Paul Vernaza, Daniel D. Lee, Seung-Joon Yi
CVPR
2009
IEEE
1133views Computer Vision» more  CVPR 2009»
15 years 4 months ago
Sparse Subspace Clustering
We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our ...
Ehsan Elhamifar, René Vidal
CVPR
2007
IEEE
14 years 11 months ago
Learning and Matching Line Aspects for Articulated Objects
Traditional aspect graphs are topology-based and are impractical for articulated objects. In this work we learn a small number of aspects, or prototypical views, from video data. ...
Xiaofeng Ren
ICIP
2005
IEEE
14 years 11 months ago
Visual tracking via efficient kernel discriminant subspace learning
Robustly tracking moving objects in video sequences is one of the key problems in computer vision. In this paper we introduce a computationally efficient nonlinear kernel learning...
Chunhua Shen, Anton van den Hengel, Michael J. Bro...
ARTCOM
2009
IEEE
14 years 4 months ago
An Efficient Bidirectional Frame Prediction Using Particle Swarm Optimization Technique
—This paper presents a Novel Bidirectional motion estimation technique, which is based on the Particle swarm optimization algorithm. Particle swarm optimization (PSO) is a popula...
D. Ranganadham, Pavan Kumar Gorpuni