A comparison of image annotation tools

In machine learning or deep learning, training a good model starts from the data that is fed…

Simulation for machine vision projects — Kapernikov

When you build a machine vision system, you expect it to perform reliably under varying conditions. To make sure it does, the system needs to be tested thoroughly. But what if the environment in which the system needs to be tested is not…

3 machine vision technologies you should know about — Kapernikov

Machine vision systems use cameras to inspect objects or to automate other perception tasks. But that doesn’t mean they use standard, visible-light cameras. In fact, many other sensor technologies are available today…

What is machine vision? — Kapernikov

Did you know that 80% of the information we need to perceive the world around us comes from our eyes? So, it’s safe to say that vision is by far the most important of the human senses. And what ingenious machinery is behind our eyesight. Our eyes collect various…

3 ways to improve your AI machine vision results

The success of an AI-driven machine vision system depends on the interaction of different factors. In this article, we discuss three ways to influence and improve AI machine vision performance.

AI on the Edge: why you (don’t) need to run your machine vision project in the cloud -

Machine vision systems typically collect large amounts of data that need to be stored and analyzed in order to make a decision. To do this effectively and at the lowest cost…

Telraam: counting traffic with AI on affordable Edge devices — Kapernikov

DevOps & data protection: how to work agile with sensitive data — Kapernikov

At Kapernikov, we love to…