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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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Publication type Article: Journal article
Document type Scientific Article
Keywords Hierarchical Temporal Memory (htm) ; Hyperspectral Imaging ; Material Classification ; Spectroscopic Sensor; Pattern-Recognition; Quality-Control; Sensor; Model
ISSN (print) / ISBN 1530-437X
Journal IEEE Sensors Journal
Quellenangaben Volume: 12, Issue: 9, Pages: 2767-2775
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Reviewing status Peer reviewed
Institute(s) Institute of Biological and Medical Imaging (IBMI)