Generation of Image Data:
The usage of many different sensors is supported (e.g. color, mono, CCI, 3D profile, miscellaneous detectors). Additionally, these sensors can also be combined to allow an even greater application range.

Object Generation:
The image data is pre-processed (e.g. the background is discarded) and objects are generated automatically from this data. From this point on, each object is evaluated separately.

Object Features:
The relevant properties (features) of each object are analyzed. Typical features are: color, size, contour, patterning and/or chemical composition.

Object Classification:
The advantages of machine learning are used to assign the objects to different categories (classes) depending on their features (optional).

Object Stream:
The results of the analysis of the object features (and the classification) are output.

Data Processing:
The data of the Object Stream is processed – e.g. to generate sorting signals or for statistical purposes (optional). This is achieved through additional apps.