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Automated segmentation of μ‐XRF image sets
Author(s) -
Vekemans B.,
Janssens K.,
Vincze L.,
Aerts A.,
Adams F.,
Hertogen J.
Publication year - 1997
Publication title -
x‐ray spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
eISSN - 1097-4539
pISSN - 0049-8246
DOI - 10.1002/(sici)1097-4539(199711/12)26:6<333::aid-xrs231>3.0.co;2-d
Subject(s) - principal component analysis , cluster analysis , pattern recognition (psychology) , artificial intelligence , segmentation , multivariate statistics , computer science , calibration , image (mathematics) , image segmentation , data mining , mathematics , statistics , machine learning
The combined use of principal component analysis (PCA) and K ‐means clustering (KMC) for the segmentation and (semi‐)quantitative calibration of multivariate μ‐XRF data sets is proposed and evaluated. The usefulness of the method is compared with that of using PCA or KMC separately by discussing image sets derived from geological and archeometric samples. © 1997 John Wiley & Sons, Ltd.