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Author Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa
Title Melamine faced panels defect classification beyond the visible spectrum. Type Journal Article
Year 2018 Publication In Sensors 2018 Abbreviated Journal
Volume Vol. 11 Issue (up) Issue 11 Pages
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.
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Call Number gtsi @ user @ Serial 89
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