Computer Vision

Computer Vision
Computational methods for analyzing visual data

Computer vision is a field concerned with extracting information from images and video. In robotics and other physical AI systems, computer vision may contribute to object recognition, obstacle awareness, localization, inspection and manipulation. It uses a range of techniques from geometric image processing to machine learning.

Tasks

Typical vision tasks include image classification, object detection, segmentation, pose estimation and tracking. Outputs are estimates, not infallible statements about the world, and may be sensitive to the training data and scene conditions.

Integration with robotics

A robot can combine camera information with other measurements to infer nearby objects or its own location. A vision-language-action model, such as RT-2, connects visual and language inputs with action representations in one research approach.

Reliability considerations

Lighting, blur, obstruction, unexpected objects, and adversarial input can reduce performance. Physical systems often require multiple checks, explicit operating limits and human supervision for higher-risk actions.

Further reading

• AI Risk Management Framework — NIST

See also

• Sensor Fusion
• Edge AI
• Embodied AI
• Robot Foundation Models
• Autonomous Systems
• Machine Learning