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.
A 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 assignment of component score images to colour channels, a perception is gained.
The correlation of selected spectra is calculated to 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).
- E.g. apply pre-processing and in parallel compare results with your expectations
- E.g. crop the scene to focus only on objects of interest.
- E.g. set the scaling to selected spectra to arrange the colour contrast in the result
- Assign those components to colour channels which best describe application relevant information.
- Save the model for later usage (e.g. to setup the live streaming)
Be sure to select pure components.
Found 2 search result(s) for "correlate method".
... Without preprocessing Set Intensity in the HS Preprocessing section and select the CCI modeling method Correlate. In the following correlation score images are shown: Sausage Plastic1 Plastic2 background sausageplasticsintenscorr1.pn ... ...
Oct 10, 2019
... 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 ...
Oct 10, 2019
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