Machine vision
Machine vision
Machine vision definition
Machine vision is a smart technology that lets computers see and understand what they’re seeing (similar to human vision). Machine vision uses cameras and specialized software to analyze images, make decisions, recognize things or people, and complete tasks. Machine vision is becoming more advanced by the minute, with new developments and capabilities being developed by data scientists around the world.
See also: adversarial machine learning, machine learning
How does machine vision work?
- The first step is collecting relevant data. For example, if you’re designing a car recognition system, it could be images of various cars.
- Then comes data preparation (removing irrelevant information and splitting it into parts for teaching and testing).
- Next, you’ll need to identify the key characteristics (like car models or colors) the model should recognize.
- The next stage is choosing a suitable machine learning (ML) algorithm for the task.
- Then, the machine learning model is given the collected data to use as learning material. The learning isn’t autonomous — data scientists oversee the process to ensure it all works correctly.
- After the model has used the materials to learn, specialists assess its performance and make adjustments to improve its accuracy.
- Once trained, the model can autonomously make decisions based on data it hasn’t seen before. For example, it can classify images as containing a car or not.
- Engineers use the trained model in real-world applications to automate tasks, like recognizing objects or making recommendations.
Examples of machine vision
- Self-driving cars.
- Smart drones.
- Medical imaging (analyzing X-rays, MRIs, and CT scans).
- Virtual reality games.