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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Raw material classification by means of hyperspectral imaging and hierarchical temporal memories.
IEEE Sens. J. 12, 2767-2775 (2012)
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
ISSN (print) / ISBN
1530-437X
e-ISSN
1558-1748
Zeitschrift
IEEE Sensors Journal
Quellenangaben
Band: 12,
Heft: 9,
Seiten: 2767-2775
Verlag
Institute of Electrical and Electronics Engineers (IEEE)
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed