| Video |
Topics |
Topic starts at
[min:sec] |
| First Impressions |
first impressions; a collage of some ImageLab functions |
00:00 |
Introduction to ImageLab
(64 min) |
| introduction and welcome | 00:07 |
| goals of the webinar | 02:08 |
| overwiew: how to build a classifier | 01:12 |
| what is ImageLab? | 04:09 |
| the ImageLab workflow | 05:35 |
| importing the data | 09:32 |
| 2D imager | 10:49 |
| image stack | 18:39 |
| preprocessing the data | 24:26 |
| spectral descriptors | 32:46 |
| chemoetrics toolbox | 43:00 |
| principal component analysis | 44:34 |
| color setup/editor | 57:17 |
| pixel masks | 58:11 |
| ImageLab scripts | 59:09 |
LIBS Tool
(7 min) |
| general remarks on full LIBS spectra | 00:05 |
| LIBS tool | 01:16 |
| zooming the spectrum | 01:55 |
| database of optical emission lines | 02:45 |
| assigning spectral lines | 04:37 |
| spectral neighborhood | 06:20 |
Classifiers
(31 min) |
| building a classifier from scratch | 00:00 |
| fruits basket hyperspectral image | 00:30 |
| overwiew: how to build a classifier | 01:12 |
| trimming the data | 03:14 |
| rescaling the image data | 05:18 |
| smoothing the specta | 06:49 |
| resampling the image | 09:25 |
| calculating the derivatives | 10:50 |
| defining spectral descriptors | 15:00 |
| specifiying the training data | 19:39 |
| creating a random forest classifier | 24:39 |
| image fusion of the classification result with a photo of the scene | 26:33 |
| creating a PLS/DA based classifier | 28:01 |
Theoretical Aspects of
Spectral Descriptors
(25 min) |
| spectral descriptors, introduction | 00:43 |
| raw intensities | 00:43 |
| types of spectral descriptors | 02:30 |
| chemical knowledge | 09:49 |
| data space transformation | 09:49 |
| transformation of the data space | 09:49 |
| spectral descriptors, examples | 12:57 |
| benefits and disadvantages of spectral descriptors | 17:56 |
Principal Component Analysis
(16 min) |
| spectral descriptors, introduction | 00:43 |
| raw intensities | 00:43 |
| types of spectral descriptors | 02:30 |
| chemical knowledge | 09:49 |
| data space transformation | 09:49 |
| transformation of the data space | 09:49 |
| spectral descriptors, examples | 12:57 |
| benefits and disadvantages of spectral descriptors | 17:56 |
Spectral Filtering
(11 min) |
| introduction, explanation of the sample | 00:00 |
| multi-sensor image (EDX and Raman) | 02:05 |
| use of PCA | 02:25 |
| marking data in the score plot | 04:02 |
| masking empty pixels | 05:05 |
| interpretation of the loadings | 08:00 |
| cluster analysis of the loadings | 09:45 |
| PCA + image stack | 12:14 |
Color Settings
(7 min) |
| contour plot properties | 00:50 |
| predefined colors | 01:44 |
| color palette | 01:55 |
| user defined colors | 01:55 |
| intensity distribution of pixels | 03:11 |
| contrast settings | 04:07 |
| auto color range | 04:20 |
| color palette editor | 04:45 |
Spike Detection and Removal
(5 min) |
| maximum map | 01:20 |
| spike removal tool | 02:05 |
License Installation
(3 min) |
license installation |
00:00 |
Appendix 
