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A Dipolar Competitive Neural Network for Video Segmentation

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 5290)

Abstract

This paper present a video segmentation method which separate pixels corresponding to foreground from those corresponding to background. The proposed background model consists of a competitive neural network based on dipoles, which is used to classify the pixels as background or foreground. Using this kind of neural networks permits an easy hardware implementation to achieve a real time processing with good results. The dipolar representation is designed to deal with the problem of estimating the directionality of data. Experimental results are provided by using the standard PETS dataset and compared with the mixture of Gaussians and background subtraction methods.

Keywords

  • Input Pattern
  • Synaptic Potential
  • Video Segmentation
  • Shadow Detection
  • Background Subtraction Method

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-540-88309-8_11
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© 2008 Springer-Verlag Berlin Heidelberg

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Luque, R.M., López-Rodríguez, D., Dominguez, E., Palomo, E.J. (2008). A Dipolar Competitive Neural Network for Video Segmentation. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_11

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  • DOI: https://doi.org/10.1007/978-3-540-88309-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88308-1

  • Online ISBN: 978-3-540-88309-8

  • eBook Packages: Computer ScienceComputer Science (R0)