Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Dynamic Appearance Modeling for Human Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Object Tracking Using Globally Coordinated Nonlinear Manifolds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Adaptive view-based appearance models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
Having a good description of an object's appearance is crucial for good object tracking. However, modeling the whole appearance of an object is difficult because of the high dimensional and nonlinear character of the appearance. To tackle the first problem we apply nonlinear dimensionality reduction approaches on multiple views of an object in order to extract the appearance manifold of the object and to embed it into a lower dimensional space. The change of the appearance of the object over time then corresponds to a walk on the manifold, with view prediction reducing to a prediction of the next step on the manifold. An inherent problem here is to constrain the prediction to the embedded manifold. In this paper, we show an approach towards solving this problem by applying a special mapping which guarantees that low dimensional points are mapped only to high dimensional points lying on the appearance manifold.