---
title: "What is computer vision?"
description: "Software that turns images and video into structured information a system can act on."
url: https://lidvizion.ai/resources/basics/what-is-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Basics](https://lidvizion.ai/resources/) Vision

# What is computer vision?

Software that turns images and video into structured information a system can act on.

## The short version

Computer vision is the part of AI that reads pixels. A model looks at an image or a video frame and returns something structured: a label, a box around an object, the outline of a shape, a set of body landmarks, or text it found.

That output is only useful when something downstream uses it — a count, an alert, a review queue, a report.

## Common tasks

Most vision systems combine a few standard tasks:

- Classification: what is in this image?
- Detection: where is each object, as a bounding box?
- Segmentation: which exact pixels belong to each object?
- Pose estimation: where are the joints of a person, frame by frame?
- OCR and document understanding: what text is here, and which field is it?

## What makes it work in production

The model is rarely the hard part. Reliable systems depend on consistent capture (camera angle, lighting, resolution), labelled examples that look like real conditions, a clear rule for low-confidence results, and a person who reviews the cases the model is unsure about.

Put it to work

## Build the missing piece

Custom tools and applications for the gaps that matter.

[How we do it](https://lidvizion.ai/services/custom-software/)

Keep reading

## More basics.

[All basics](https://lidvizion.ai/resources/)

- [Image vs. video processing](https://lidvizion.ai/resources/basics/image-vs-video-processing/): When a single frame is enough, and when you need motion, sequence and time.
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Fine-tuning vs. pre-trained models](https://lidvizion.ai/resources/basics/model-fine-tuning-vs-pre-trained-models/): Why most teams should start with a capable pre-trained model, and when fine-tuning earns its cost.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)