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PCA - Principal Component Analysis
Theory
An introduction to what PCA can do as far as images are concerned is
in:
David S. Bright, Principal Component Analysis
, Scatter Diagrams and Color Overlays for analysis of Compositional Maps,
MICROBEAM ANALYSIS 1995, pp 403-4, VCH, NY.
Practise
Superconductor Precursor Example
Functions
 |
| windows -> sb16 stack |
| doit |
| -- |
| print Eigenvalues |
| Print P matrix |
| Display P matrix |
| Colorize P matrix |
| -- |
| Reconstruct Data |
| plot P matrix |
|
- windows -> sb16 stack This is
puts the images into a special gray level image stack
so that 'doit' will be able to make a similar stack of the principal component
(eigenvalue) images. This PCA routine (need references) mean centers the
(integer) data. The PC images and reconstructed images are also mean centered
- scale to byte
before writing a file for exporting to Image or Photoshop.
- Doit Perform the principal component analysis.