TY  - JOUR
AU  - Cristhian A. Aguilera-Carrasco
AU  - C. Aguilera
AU  - Angel Sappa
PY  - 2018//
TI  - Melamine Faced Panels Defect Classification beyond the Visible Spectrum
T2  - SENS
JO  - Sensors
SP  - 1
EP  - 10
VL  - 18
IS  - 11
KW  - industrial application
KW  - infrared
KW  - machine learning
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/AAS2018.pdf
UR  - http://dx.doi.org/10.3390/s18113644
N1  - MSIAU; 600.122
ID  - Cristhian A. Aguilera-Carrasco2018
ER  -