GNG based foot reconstruction for custom footwear manufacturing

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/62670
Información del item - Informació de l'item - Item information
Title: GNG based foot reconstruction for custom footwear manufacturing
Authors: Jimeno-Morenilla, Antonio | Garcia-Rodriguez, Jose | Orts-Escolano, Sergio | Davia-Aracil, Miguel
Research Group/s: UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante | Informática Industrial y Redes de Computadores | Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Custom footwear manufacturing | Foot reconstruction | Growing neural gas | Marching cubes
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: Jan-2016
Publisher: Elsevier
Citation: Computers in Industry. 2016, 75: 116-126. doi:10.1016/j.compind.2015.06.002
Abstract: Custom shoes manufacturing is one of the major challenges facing the footwear industry today. A shoe for everyone: it is a change in the production model in which each individual’s foot is the main focus, replacing traditional size systems based on population means. This paradigm shift represents a major effort for the industry, for which the design and not production becomes the main bottleneck. It is therefore necessary to accelerate the design process by improving the accuracy of current methods. The starting point for making a shoe that fits the client’s foot anatomy is scanning the surface of the foot. Automated foot model reconstruction is accomplished through the use of the self-organising growing neural gas (GNG) network, which is able to topographically map the low dimension of the network to the high dimension of the manifold of the scanner acquisitions without requiring a priori knowledge of the structure of the input space. The GNG obtains a surface representation adapted to the topology of the foot, is accurate, tolerant to noise, and eliminates outliers. It also improves the reconstruction in “dark” areas where the scanner does not obtain information: the heel and toe areas. The method reconstructs the foot surface 4 times more accurately than other well-known methods. The method is generic and easily extensible to other industrial objects that need to be digitized and reconstructed with accuracy and efficiency requirements.
Sponsor: This work was partially funded by the Spanish Government DPI2013-40534-R grant, supported with Feder funds, NILS Mobility Project 012-ABEL-CM-2014A, and Fundación Séneca 18946/JLI/13.
URI: http://hdl.handle.net/10045/62670
ISSN: 0166-3615 (Print) | 1872-6194 (Online)
DOI: 10.1016/j.compind.2015.06.002
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2015 Elsevier B.V.
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.compind.2015.06.002
Appears in Collections:INV - I2RC - Artículos de Revistas
INV - RoViT - Artículos de Revistas
INV - UNICAD - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
Thumbnail2016_Jimeno_etal_CompInd_final.pdfVersión final (acceso restringido)4,24 MBAdobe PDFOpen    Request a copy
Thumbnail2016_Jimeno_etal_CompInd_preprint.pdfPreprint (acceso abierto)1,98 MBAdobe PDFOpen Preview


Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.