Next Article in Journal
Review of Incoherent Broadband Cavity-Enhanced Absorption Spectroscopy (IBBCEAS) for Gas Sensing
Next Article in Special Issue
Temperature Measurement Method for Blast Furnace Molten Iron Based on Infrared Thermography and Temperature Reduction Model
Previous Article in Journal
Speckle Noise Suppression in SAR Images Using a Three-Step Algorithm
Previous Article in Special Issue
Miniature Uncooled and Unchopped Fiber Optic Infrared Thermometer for Application to Cutting Tool Temperature Measurement

Melamine Faced Panels Defect Classification beyond the Visible Spectrum

Universidad Tecnológica de Chile INACAP, Av. Vitacura 10.151, Vitacura 7650033, Santiago, Chile
University of Bío-Bío, DIEE, Concepción 4051381, Concepción, Chile
Computer Vision Center, Edifici O, Campus UAB, Bellaterra, 08193 Barcelona, Spain
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.
Sensors 2018, 18(11), 3644;
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)
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
Show Figures

Figure 1

MDPI and ACS Style

Aguilera, C.A.; Aguilera, C.; Sappa, A.D. Melamine Faced Panels Defect Classification beyond the Visible Spectrum. Sensors 2018, 18, 3644.

AMA Style

Aguilera CA, Aguilera C, Sappa AD. Melamine Faced Panels Defect Classification beyond the Visible Spectrum. Sensors. 2018; 18(11):3644.

Chicago/Turabian Style

Aguilera, Cristhian A., Cristhian Aguilera, and Angel D. Sappa 2018. "Melamine Faced Panels Defect Classification beyond the Visible Spectrum" Sensors 18, no. 11: 3644.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop