Computer vision is the area of artificial intelligence that extracts meaning from visual data. Typical tasks include image classification, object detection, segmentation, optical character recognition (OCR), and tracking motion in video.
Why it matters
It powers photo search, quality inspection in factories, document scanning, medical image analysis, and driver assistance. Most modern systems use deep learning, particularly convolutional networks or vision transformers. Accuracy usually depends on how closely the training images resemble the images seen in production.
Example
An object detector returns labeled boxes with confidence scores:
[{"label": "person", "score": 0.97, "box": [34, 20, 210, 400]},
{"label": "dog", "score": 0.88, "box": [220, 180, 340, 390]}]Applications choose a score threshold to balance missed objects against false alarms.
Common mistakes
- Training on clean studio images and expecting accuracy on dark, blurry real-world input.
- Ignoring privacy and consent rules when processing images of people.
- Reporting accuracy without checking performance across lighting, skin tones, and camera types.
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