A scene is described as a mix of pure components (constituents). The resulting perception is based on correlation information of pure components to the scene.
Spectra can be a mix of different spectra of pure components. By knowledge of „all“ pure components, a correlation value per component and pixel can be calculated. By assigning component score images to color channels, a perception is gained.
The correlation of selected spectra is calculated for the selected HS cube. The value per component (spectra) is shown through a score image (correlation image) at the bottom right of the Model perspective.
By assignment to color channels, a CCI image is obtained (shown to the right of the data view).
- Select a spectra set per pure component available in the scene. Hint: give the spectra a meaningful name.
- Inspect gained component score images (match with your expectations).
- Apply preprocessing and in parallel compare results with your expectations.
- Crop the scene to focus only on objects of interest.
- Set the scaling to selected spectra to arrange the color contrast in the result.
- Assign those components to color channels which best describe application relevant information.
- Save the model for later usage (e.g. to set up the live streaming).
Make sure to select pure components.
Found 3 search result(s) for "correlate method".
... obtained. Without preprocessing Set Intensity in the Preprocessing section and select the CCI modelling method Correlate. In the following correlation score images are shown: Sausage Plastic1 Plastic2 background sausageplasticsintenscorr1.pn ... ...
Jun 18, 2020
... Method: unsupervised method showing spectroscopic differences in different colors. CCI Extract Method: extraction based on variance information. CCI Correlate Method: correlation of pure substances CCI Constrain Method: constraining colors CCI Design Method: design of CCImethod ...
May 27, 2020
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