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|Title:||The fusion of deep learning architectures and particle filtering applied to lip tracking|
|Citation:||Proceedings of 20th International Conference on Pattern Recognition (ICPR), 2010 / pp.2065-2068|
|Publisher:||IEEE Computer society|
|Conference Name:||International Conference on Pattern Recognition (20th : 2010 : Istanbul, Turkey)|
|Gustavo Carneiro and Jacinto C. Nascimento|
|Abstract:||This work introduces a new pattern recognition model for segmenting and tracking lip contours in video sequences. We formulate the problem as a general nonrigid object tracking method, where the computation of the expected segmentation is based on a filtering distribution. This is a difficult task because one has to compute the expected value using the whole parameter space of segmentation. As a result, we compute the expected segmentation using sequential Monte Carlo sampling methods, where the filtering distribution is approximated with a proposal distribution to be used for sampling. The key contribution of this paper is the formulation of this proposal distribution using a new observation model based on deep belief networks and a new transition model. The efficacy of the model is demonstrated in publicly available databases of video sequences of people talking and singing. Our method produces results comparable to state-of-the-art models, but showing potential to be more robust to imaging conditions.|
|Rights:||© 2010 IEEE|
|Appears in Collections:||Computer Science publications|
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