This example project contains Hyperpsectral imaging data of sausage contaminated with pieces of plastics.
|Table of Contents|
|Original Donor||Measurement System||Release Date|
|2016-05-30||Sausage contaminated with plastics.exe||20.38 MB|
The example project "Sausage contaminated with plastics" contains Hyperpsectral imaging data of sausage contaminated with pieces of plastics.
|Title||Sausage contaminated with plastics|
|Category||Food - Impurity detection|
|Sample Description||2x2 plastics on the top of a sausage stack ("Extrawurst").|
|Date of Measurement||16-04-27|
|Measurement System||STEMMER IMAGING HS Setup: Allied Vision GoldenEye CL033SWIR, Specim N17E slit30um, KOWA F12.5, Perception System|
|Measurement Description||Measurement of reflectance|
|Measurement Setup||Illumination: Halogen diffuse, background: black foam|
This data set gives an example on industrial impurity detection. So the discrimination of objects (impurities) from product (sausage) can be investigated.
Further notes of the data sets donor
Beside the sausage_plastics (979-1652nm) an additional hyperspectral cube is available named sauasage_plastics_highSpeed. The "highSpeed" data set was obtained by measuring the same object but in a reduced spectral range: 1071-1268nm (resolved by 121 spectral points).
The "highSpeed" data set shows the possibility to speed up the camera system (scan rate) by decreasing the number of spectral points to be read from the sensor.
High speed measurement is mandatory for this kind of industrial application (impurity detection) since the transport speed of objects is typically high (1-2m/s) and size of impurities typically small (in the millimeter range and smaller).
Mentioned in Tutorials
Good to Know
- When downloaded execute the installer. The data will get installed in the examples folder of your Perception Park folder in your user folder (e.g. my documents).
- When installed, open this project from the Start perspective of the Perception Studio program.
- A demo version of the Perception Studio program is available from here.
- You are welcome to add your example data to the download space - see How to add example data to the Perception Wiki