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https://hdl.handle.net/2440/84266
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Type: | Conference paper |
Title: | The fusion of deep learning architectures and particle filtering applied to lip tracking |
Author: | Carneiro, G. Nascimento, J. |
Citation: | Proceedings of 20th International Conference on Pattern Recognition (ICPR), 2010 / pp.2065-2068 |
Publisher: | IEEE Computer society |
Publisher Place: | USA |
Issue Date: | 2010 |
ISBN: | 9781424475421 |
ISSN: | 1051-4651 |
Conference Name: | International Conference on Pattern Recognition (20th : 2010 : Istanbul, Turkey) |
Statement of Responsibility: | 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 |
DOI: | 10.1109/ICPR.2010.508 |
Published version: | http://dx.doi.org/10.1109/icpr.2010.508 |
Appears in Collections: | Aurora harvest Computer Science publications |
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