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Rodriguez-Cobo, L.* ; Garcia-Allende, P. ; Cobo, A.* ; Lopez-Higuera, J.M.* ; Conde, O.M.*

Raw material classification by means of hyperspectral imaging and hierarchical temporal memories.

IEEE Sens. J. 12, 2767-2775 (2012)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
The recently proposed hierarchical temporal memory (HTM) paradigm of soft computing is applied to the detection and classification of foreign materials in a conveyor belt carrying tobacco leaves in a cigarette manufacturing industry. The HTM has been exposed to hyperspectral imaging data from 10 types of unwanted materials intermingled with tobacco leaves. The impact of the HTM architecture and the configuration of internal parameters on its classification performance have been explored. Classification results match or surpass those attained with other methods, such as Artificial Neural Networks (ANNs), with the advantage that HTM are able to handle raw spectral data and no preprocessing, spectral compression, or reflectance correction is required. It is also demonstrated that an optimized configuration of the HTM architecture and internal values can be derived from the statistical properties of the hyperspectral data, allowing the extension of the approach to other classification problems.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Hierarchical Temporal Memory (htm) ; Hyperspectral Imaging ; Material Classification ; Spectroscopic Sensor; Pattern-Recognition; Quality-Control; Sensor; Model
Sprache englisch
Veröffentlichungsjahr 2012
HGF-Berichtsjahr 2012
ISSN (print) / ISBN 1530-437X
e-ISSN 1558-1748
Zeitschrift IEEE Sensors Journal
Quellenangaben Band: 12, Heft: 9, Seiten: 2767-2775 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
DOI 10.1109/JSEN.2012.2202898
WOS ID WOS:000306993700007
Scopus ID 84864537080
Erfassungsdatum 2012-08-30
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