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Multivariate image analysis

Artyushkova K, Fulghum JE (2002) Multivariate image analysis methods applied to XPS imaging data sets. Surface Interface Anal 33 185... [Pg.282]

Geladi P, Grahn H (1996) Multivariate image analysis. Wiley, Chichester... [Pg.284]

Geladi P, Grahn H, Esbensen K, Bengtsson E (1992b) Image analysis in chemistry. II. Multivariate image analysis. Trends Anal Chem 11 121... [Pg.285]

P. Geladi and H. Grahn, Multivariate Image Analysis, John Wiley Sons, Chichester, 1996. [Pg.279]

Multivariate Image Analysis Strong and Weak Multiway Methods Strong and weak -way methods analyze 3D and 2D matrices, respectively. Hyperspectral data cube structure is described using chemometric vocabulary [17]. A two-way matrix, such as a classical NIR spectroscopy data set, has two modes object (matrix lines) and V variables (matrix columns). Hyperspectral data cubes possess two object modes and one variable mode and can be written as an OOV data array because of their two spatial directions. [Pg.418]

Geladi, P. 8c Grahn, H., Multivariate Image Analysis John Wiley 8c Sons Chichester, 1996. [Pg.225]

Analytical Problem Solving Selection of Analytical Methods Multivariate Image Analysis Quality Assurance in Analytical Chemistry Quantitative Spectroscopic Calibration... [Pg.21]

Nattkemper, T.W. 2004. Multivariate image analysis in biomedicine. J. Biomed. Inform. 37 380-391. [Pg.186]

Paul Geladi, Multivariate Image Analysis in Chemistry and Related Areas Chemometric Image Analysis, Wiley, Chichester, 1996. [Pg.288]

Multivariate Image Analysis (MIA) and Principal Component Analysis (PCA)... [Pg.72]

Dunn, K.G. (2007) M.A.C.C. Multivariate Image Analysis (MACCMIA). Manual and Tutorial for Version 1.81. http //macc. mcmaster.ca (accessed 19 November 2008)... [Pg.107]

Figure 8.11 A multispectral wood image, (a) Pseudocolor image (b) Score plot (c) Multivariate image analysis (MIA) combined score image (d) Feedback Multivariate Model Selection (FEMOS) nine-class segmented image. Illustration reproduced from Ref [53]. Figure 8.11 A multispectral wood image, (a) Pseudocolor image (b) Score plot (c) Multivariate image analysis (MIA) combined score image (d) Feedback Multivariate Model Selection (FEMOS) nine-class segmented image. Illustration reproduced from Ref [53].
The visualization of large data sets has special requirements. A typical example area is multivariate image analysis, especially in remote sensing, but hyperspectral imaging is also moving into other fields. Another is three-dimensional visualization of weather systems. [Pg.216]

Geladi P, Grahn H, Multivariate Image Analysis, in Encyclopedia of Analytical Chemistry, Meyers R (ed.), John Wiley Sons, Ltd, Chichester, 2000, pp. 13540-13562. [Pg.356]

Yu, H., and MacGregor, J. F. "Monitoring Flames in an Industrial Boiler Using Multivariate Image Analysis." American Institute of Chemical Engineering Journal 50 (2004) 1474-83. [Pg.354]

MIA multivariate image analysis MMA methyl methacrylate MSE multi-screw extruder... [Pg.1013]

Lee, J.L.S., Gilmore, I.S., Fletcher, I.W., Seah, M.P. (2009) Multivariate image analysis strategies for ToF-SIMS images with topography. Surf. Interface Anal., 41, 653-665. [Pg.255]

IX. RAMAN IMAGE PROCESSING A. Multivariate Image Analysis... [Pg.227]

Raman chemical imaging is performed from 1260 to 1400 cm in order to differentiate CPO, EPR, and PP [52]. Even though the CPO Raman band at 1300 cm significantly overlaps the EPR band, it is sufficiently broad to be detected using multivariate image analysis techniques, including CCA. [Pg.243]

After Geladi and Grahn [13]. Reprinted from R Geladi etaL, Multivariate Image Analysis, Cr yright 1996 John WQey Sons, Limited. Reproduced with pmnission. [Pg.517]


See other pages where Multivariate image analysis is mentioned: [Pg.555]    [Pg.209]    [Pg.437]    [Pg.417]    [Pg.183]    [Pg.184]    [Pg.470]    [Pg.65]    [Pg.219]    [Pg.3]    [Pg.169]    [Pg.201]    [Pg.243]    [Pg.252]    [Pg.947]    [Pg.50]    [Pg.434]    [Pg.261]   
See also in sourсe #XX -- [ Pg.418 ]

See also in sourсe #XX -- [ Pg.65 , Pg.72 ]




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