Machine vision is one of the driving forces of industrial automation. For a long time, it’s been primarily pushed forward by improvements made in 2D image sensing and, for some applications, 2D sensing is still an optimal tool to solve a problem. But the majority of challenges machine vision is facing today has a 3D character. From well-established metrology up to new applications in smart robotics, 3D sensors serve as a main source of data. Here, we discuss the parameters of 3D sensing techniques.
Sensors are the heart of every vision system! From industrial automation to image processing, human-machine-interaction, or self driving cars - selecting the right sensor depends very much on the application and the desired output. Six important criteria will help you to choose a specification and select the optimal image sensor for you application! Enter your details below, or click here to download the white paper for free: https://imaging.framos.com/whitepaper-sensors
Imaging lenses used in many industrial machine vision applications have special requirements beyond those of standard imaging lenses. The lenses used in factory automation, robotics, and industrial inspection have to work in specific and demanding environments, which could involve vibrations, shocks, temperature changes, and contaminants. Because of these environmental requirements, new classes of ruggedized lenses are being designed specifically to work in a multitude of different scenarios, therefore creating different types of ruggedization. There are three distinct types of ruggedization available: Industrial Ruggedization, Ingress Protection Ruggedization, and Stability Ruggedization.
This technical note is an intermediate level document intended
to provide guidelines to systems engineers for determining the
resolution requirements for electronic imaging systems. We’ll do
this with an emphasis on:
a) the correct goals for each application, and
b) taking the total system (imaging chain) into account.
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.
Greg Blackman asks what it takes to commercialise new imaging technology
Embedded vision, deep learning, and Industry 4.0 could all have a big impact on the machine vision sector in the future. Three experts give their opinions
Andrew Williams explores the production and automation markets in China, India and other fast-growing nations
Pierre Cambou, imaging activity leader at Yole Développement, analyses the merger and acquisition landscape for machine vision
Following a successful European Machine Vision Forum, which brought together representatives from industry and research, Professor Bernd Jähne at the HCI, Heidelberg University and a board member of the European Machine Vision Association, argues collaboration between industry and academia is now more important than ever