Realtime Kernel based Machine Learning Template Matching (KMLT)
This paper deals with a new approach for the problemof realtime planar templatematching. We consider tracking as the estimation of a parametric function between observations and motion and we propose an extension of the learning based approach presented simultaneously by Cootes and al. and by Jurie...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Computer Vision Center Press
2009-04-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| Subjects: | |
| Online Access: | https://elcvia.cvc.uab.es/article/view/224 |
| Summary: | This paper deals with a new approach for the problemof realtime planar templatematching. We consider
tracking as the estimation of a parametric function between observations and motion and we propose an
extension of the learning based approach presented simultaneously by Cootes and al. and by Jurie and
Dhome to non linear regression functions. The estimation of the linear parameters associated to the basis
functions (kernel functions) of the model is then achieved using a training set of motions and associated
observations. We show that the resulting method outperforms the robustness of the linear tracker against
noisy observations. |
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| ISSN: | 1577-5097 |