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Beleuchtungsinvariante Kontourerkennung - Beleuchtungsinvariante Bewegungsdetektion - Hyperbeldetektion - Informationsraumanalyse - CI basierte Identifikatoren - Datenkategorisierer - Alarmierungsroutinen - Maschinenüberwachung - Harmonischenanalyse - DLS-Analysatoren - FD-Analysatoren - CI-Alarmierungsroutinen - Trendanalyse - neuronale Qualitätskontrolle - Sensorfusion - neuronale Prädiktoren - Analyse biologischer Signale - neuronal basierte Identifikatoren - akustische Mustererkennung - Trendanalyse - CI basierte Personenkategorisierung

Welcome to our website!

Find out more about us and our products here, in which you can see some of our applications in action!

All applications work with the same bionic filter, modelling the structure of the first 3 layers of the retina. That means: no learning procedures, no parameter adaptation! Simply integrate the filter in your existing software and "see" the lighting invariant.

Do you see interfaces to your areas of responsibility?

But what does independent of lighting contour detecting mean in detail?

Well, for example, to reliably detect text and/or scratches under extreme lighting conditions

Using this basic technology the following applications are implicite solvebale:

The contacless, independent of lightning, quality control - of course with virtual collision control for safe interaction between human and robot.

Or, illumination inversion based contour and motion detection for safe interaction between robot and human being under extremely fluctuating lighting conditions by defining virtual danger protection areas.

...or in a slightly modified version: lighting invariant contour recognition to support autonomous driving. Left original, right contour-filter representation.

Technical details of the filters:

Necessary external libraries/frameworks: none

Operating system independent

Language: C

Shown Example: Quasi-3D object representation during car driving.

Camera: Logitec; used hardware: Asus Laptop

Processingtime/frame: 31 ms

.. or to increase the image quality through online post-processing of image material recorded during AVU trips. (Film on the left: original camera material and camera material optimized by a bionic filter. Right: original camera material and a quasi-3D object representation generated by a bionic filter.)