more robust machine vision applications.

In many applications, machine vision is part of a bigger system. For example autonomous navigation, identification of instruments during laparoscopic surgery or monitoring crops on a farm. With the rise of machine learning, we now regularly autonomously execute complex tasks like image classification, object detection, and image segmentation.


  • Development of integrated machine vision applications for object classification-, segmentation- and detection
  • Enabling machine vision where the right data is not available through synthetic data
  • Evaluating different sensor and lighting setups in simulation during development through simulation

developing vision-in-the-loop systems.

Building a full system that takes into account the dependencies between components can be challenging. With our system engineering and domain knowledge we can develop vision-in-the-loop systems, where machine vision is part of a bigger system, to deliver optimal performance and functionality.



synthetic data.

Data availability is a big obstacle in machine vision. This is where our expertise on synthetic data generation can offer a solution, enabling us to develop even more robust machine learning models. Synthetic data is generated through simulation, offering new paths to machine learning currently limited by a lack of the right data. For more information on this fascinating subject, check out the link below.

More about Synthetic data

David Rijlaarsdam

+31 (0)88 - 115 20 00

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synthetic data

Generate near-infinite permutations of complex, domain-specific, 3D environments

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time series analysis

Increase functionality by actionable knowledge extraction

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machine health monitoring

Optimize machine operation quality

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reinforcement learning based control

Optimize control and machine performance

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