Fast 2D/3D object representation with growing neural gas

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Title: Fast 2D/3D object representation with growing neural gas
Authors: Angelopoulou, Anastassia | Garcia-Rodriguez, Jose | Orts-Escolano, Sergio | Gupta, Gaurav | Psarrou, Alexandra
Research Group/s: Informática Industrial y Redes de Computadores | Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Minimum description length | Self-organising networks | Shape modelling | Clustering
Knowledge Area: Arquitectura y Tecnología de Computadores | Ciencia de la Computación e Inteligencia Artificial
Issue Date: May-2018
Publisher: Springer London
Citation: Neural Computing and Applications. 2018, 29(10): 903-919. doi:10.1007/s00521-016-2579-y
Abstract: This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction.
Sponsor: This work was partially funded by the Spanish Government DPI2013-40534-R Grant.
ISSN: 0941-0643 (Print) | 1433-3058 (Online)
DOI: 10.1007/s00521-016-2579-y
Language: eng
Type: info:eu-repo/semantics/article
Rights: © The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Peer Review: si
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Appears in Collections:INV - RoViT - Artículos de Revistas
INV - I2RC - Artículos de Revistas

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