OpenCV.

Where OpenCV fits on a long engagement.

Most production vision systems are mostly OpenCV with a model somewhere inside. Capture, decoding, resizing, colour conversion, denoising and geometric correction all happen before inference, and the quality of that preprocessing frequently determines the result more than the model does.

Classical methods remain the right answer more often than expected. Contour detection, template matching, thresholding and morphological operations solve well-constrained problems — controlled lighting, known geometry — deterministically and at a fraction of the compute, with the added advantage of being explainable.

What an assigned team does with OpenCV.

Real-world image conditions are the hard part of any vision system. Lighting variation, motion blur, occlusion and camera differences break systems that performed well on curated test data.

Building for those conditions from the start, rather than discovering them in deployment, is the difference between a demo and a system, and it is scoped explicitly under managed ai services.

What we use OpenCV for.

• Preprocessing that decides the outcome Correction and normalisation before inference, which often matters more than the model.
• Classical methods where conditions are controlled Deterministic, explainable detection at a fraction of the compute.
• Video handled at frame rate Efficient decoding and processing where a naive pipeline cannot keep up.

How OpenCV capacity is assigned.

Computer vision capacity is assigned inside AI work, with real-world capture conditions treated as a design input rather than a later discovery.