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Type: Conference paper
Title: Efficient search methods and deep belief networks with particle filtering for non-rigid tracking: application to lip tracking
Author: Nascimento, J.
Carneiro, G.
Citation: Proceedings of the 17th IEEE International Conference on Image Processing, held in Hong Kong, 26-29 September, 2010: pp.3817-3820
Publisher: IEEE Computer Society
Publisher Place: Online
Issue Date: 2010
Series/Report no.: IEEE International Conference on Image Processing ICIP
ISBN: 9781424479948
ISSN: 1522-4880
Conference Name: IEEE International Conference on Image Processing (17th : 2012 : Hong Kong)
Statement of
Responsibility: 
Jacinto C. Nascimento and Gustavo Carneiro
Abstract: Pattern recognition methods have become a powerful tool for segmentation in the sense that they are capable of automatically building a segmentation model from training images. However, they present several difficulties, such as requirement of a large set of training data, robustness to imaging conditions not present in the training set, and complexity of the search process. In this paper we tackle the second problem by using a deep belief network learning architecture, and the third problem by resorting to efficient searching algorithms. As an example, we illustrate the performance of the algorithm in lip segmentation and tracking in video sequences. Quantitative comparison using different strategies for the search process are presented. We also compare our approach to a state-of-the-art segmentation and tracking algorithm. The comparison show that our algorithm produces competitive segmentation results and that efficient search strategies reduce ten times the run-complexity.
Keywords: Deep belief networks; lip segmentation; optimization algorthims; search methods; tracking
Rights: © 2010 IEEE
RMID: 0020114362
DOI: 10.1109/ICIP.2010.5654045
Appears in Collections:Computer Science publications

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