Chapter title | A growing neural gas algorithm with applications in hand modelling and tracking |
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Authors | Angelopoulou, A., Psarrou, A. and García Rodríguez, J. |
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Editors | Cabestany, J., Rojas, I. and Joya, G. |
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Abstract | Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced Growing Neural Gas (GNG) model for applications in hand modelling and tracking. The modified network consists of the geometric properties of the nodes, the underline local feature of the image, and an automatic criterion for maximum node growth based on the probability of the objects in the image. We present experimental results for hands and T1-weighted MRI images, and we measure topology preservation with the topographic product. |
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Book title | Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part II |
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Year | 2011 |
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Publisher | Springer |
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Publication dates | 2011 |
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Series | Lecture notes in computer science |
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Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-21498-1_30 |
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Web address (URL) | http://www.scopus.com/inward/record.url?eid=2-s2.0-79957928857&partnerID=MN8TOARS |
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Journal citation | (6692), pp. 236-243 |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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