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Sensors 2018, 18(11), 3644; https://doi.org/10.3390/s18113644

Melamine Faced Panels Defect Classification beyond the Visible Spectrum

1
Universidad Tecnológica de Chile INACAP, Av. Vitacura 10.151, Vitacura 7650033, Santiago, Chile
2
University of Bío-Bío, DIEE, Concepción 4051381, Concepción, Chile
3
Computer Vision Center, Edifici O, Campus UAB, Bellaterra, 08193 Barcelona, Spain
4
Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CIDIS, Campus Gustavo Galindo, Km 30.5 vía Perimetral, Guayaquil 09-01-5863, Ecuador
*
Author to whom correspondence should be addressed.
Received: 5 October 2018 / Revised: 22 October 2018 / Accepted: 24 October 2018 / Published: 27 October 2018
(This article belongs to the Special Issue Infrared Sensors and Technologies)
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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. View Full-Text
Keywords: infrared; industrial application; machine learning infrared; industrial application; machine learning
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Aguilera, C.A.; Aguilera, C.; Sappa, A.D. Melamine Faced Panels Defect Classification beyond the Visible Spectrum. Sensors 2018, 18, 3644.

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