This article is brought to you by: 

Using deep learning in machine vision

More and more, machine vision systems are expected to make dynamic, automated decisions based on variable conditions. The amount of time and effort to develop these systems can be daunting. Today, the advent of deep learning is changing this landscape and putting automation within the reach of many. Resources such as open-source libraries, Nvidia hardware, and FLIR cameras are helping to make this change happen FLIR cameras have advanced features that minimize the image pre-processing required for neural network training, work seamlessly with platforms such as NVidia Jetson TX-2 and Drive PX 2, and offer 24/7 reliability for trouble-free deployment.

Premium Access
To access this content please enter your details in the fields below. If you believe you have already done so for this, please resubmit your details here.
Already registered?
If you believe you have already registered please submit the email you originally entered.
Feature

Greg Blackman explores the latest advances made in scientific CMOS sensors and asks whether CCDs still have a place in life science imaging

Feature

Denis Bulgin speaks to Mark Williamson and David Hearn, who both started their own vision companies in the UK 20 years ago and are both now at Stemmer Imaging

Feature

Matthew Dale investigates a new class of highly-efficient image sensor that’s just starting to find its way onto the commercial market, all based on the principles of biological sight

Feature

Andrew Williams on the uses and current state of hyperspectral imaging, along with the technique’s potential as an industrial inspection tool

Feature

Stemmer Imaging’s series of technology days included talks from various lens manufacturers. Here, we round up some of what was discussed at the event

Feature

Greg Blackman charts the meteoric rise of Chinese firm Hikvision, one of the top suppliers of video surveillance equipment that has now turned its sights on industrial vision