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Journal of Electronic Imaging • Open Access

Material recognition by feature classification using time-of-flight camera
Author(s): Fabio Martino; Cosimo Patruno; Nicola Mosca; Ettore Stella

Paper Abstract

We propose a method for solving one of the significant open issues in computer vision: material recognition. A time-of-flight range camera has been employed to analyze the characteristics of different materials. Starting from the information returned by the depth sensor, different features of interest have been extracted using transforms such as Fourier, discrete cosine, Hilbert, chirp-z, and Karhunen–Loève. Such features have been used to build a training and a validation set useful to feed a classifier (J48) able to accomplish the material recognition step. The effectiveness of the proposed methodology has been experimentally tested. Good predictive accuracies of materials have been obtained. Moreover, experiments have shown that the combination of multiple transforms increases the robustness and reliability of the computed features, although the shutter value can heavily affect the prediction rates.

Paper Details

Date Published: 23 August 2016
PDF: 17 pages
J. Electron. Imag. 25(6) 061412 doi: 10.1117/1.JEI.25.6.061412
Published in: Journal of Electronic Imaging Volume 25, Issue 6
Show Author Affiliations
Fabio Martino, CNR ISSIA (Italy)
Cosimo Patruno, Consiglio Nazionale delle Ricerche (Italy)
Nicola Mosca, CNR ISSIA (Italy)
Ettore Stella, National Research Council of Italy, Institute of Intelligent Systems for Automation (CNR-ISSIA) (Italy)

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