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Generate near-infinite permutations of complex, domain-specific, 3D environments
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.
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.
Generate near-infinite permutations of complex, domain-specific, 3D environments
Increase functionality by actionable knowledge extraction
Optimize machine operation quality
Optime control and machine performance