Melamine Faced Panels Defect Classification beyond the Visible Spectrum
Aguilera, Cristhian A. (Universidad Tecnológica de Chile)
Aguilera, Cristhian A (Universidad del Bío-Bío (Concepción, Xile))
Sappa, Angel Domingo (Centre de Visió per Computador (Bellaterra, Catalunya))

Date:	2018
Abstract:	In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
Grants:	Ministerio de Economía y Competitividad TIN2014-56919-C3-2-R Agencia Estatal de Investigación TIN2017-89723-P
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Infrared ; Industrial application ; Machine learning
Published in:	Sensors (Basel, Switzerland), Vol. 18, Issue 11 (October 2018) , art. 3644, ISSN 1424-8220

DOI: 10.3390/s18113644
PMID: 30373245

10 p, 4.5 MB

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