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Aurora Imaging Library™, formerly Matrox Imaging Library, machine-vision software development kit (SDK) has a deep collection of tools for image capture, processing, analysis, annotation, display, and archiving. Code-level customization starts here.
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They say there’s two sides to every story. Well, now there’s at least two viable solutions to every problem when you invite both seasoned and early career vision system engineers to the table, and that’s making it easier to see the right path forward.
For many decades, there were business problems that machine vision technology (and engineers) just couldn’t solve for manufacturers, warehouse operators, 3PLs, retailers, and even healthcare providers. Now, we’re seeing at least two solutions proposed for every problem. In some cases, we’ve swung from having no options to having too many options when it comes to using machine vision as a solution facilitator.
Admittedly, newer AI, deep learning OCR, and 3D technology capabilities are part of the reason why we’re finally able to crack the code on problems that previously stumped us. However, I would argue that it’s our new “human computing” capabilities – the critical thinking now emanating from multi-generational viewpoints and collaborations – that have really removed the hurdles to operational transformations.
Tomás Goldaracena and Robert Eastlund from Graftek Imaging, a Zebra Registered Reseller and Industrial Automation Distributor, agree.
In fact, Robert said the reason he’s still excited to go to work every day after 30+ years in the machine vision space is because the combination of new technology capabilities and fresh engineering perspectives makes it possible for him to help customers with anything and everything they want to do. This wasn’t the case even 10 years ago. Sometimes things were just “impossible,” and that was frustrating.
Now, though, Robert welcomes customers’ challenging requests because he knows his knowledge, perspectives, and technical skills pair nicely with those of early career engineers such as Tomás. Together, these two generations of engineers can answer the hard questions, simplify complex workflows, and help drive meaningful changes within customers’ businesses.
That’s not the only reason why both he and Tomás see the multi-generational “convergence of the minds” so pivotal to the machine vision space and so beneficial to you, however.
Tune into the latest episode of the Industrial Automation Insider podcast now to hear how seemingly contrasting points of view about vision system design are resulting in solution recommendations that are more compatible with your business objectives, quality and safety priorities, and growth ambitions.
You can also download the MP3 from the audio player below to listen later/offline.
I’ve also included the transcript here in case you’d prefer to read the interview.
When you have time, it would also be good to check out these other discussions around how machine vision and automation technologies can be used to solve your business problems and meet your objectives:
If you’re wondering, “How is deep learning already at work in the real world (i.e., my world)?” or “How could deep learning help me work in new, more efficient ways?” then you’ve come to the right place. This blog post serves as a primer to deep learning, explaining what it really is, how it works, and how you can make it work for your business for inspections, quality control and more.
What to Know Before You Try to Train (and Test) a Deep Learning Application
AI must fail over and over to succeed. But there are several mistakes you want to avoid making when teaching an AI-driven application how to look for anomalies on a product, component, or label using deep learning.
While AI is certainly proving helpful with quality control during the electric battery production and electric vehicle assembly processes, AI isn’t the only thing that improves inspection results. Find out what can help automotive suppliers, manufacturers, and integrators even more.
A manufacturing veteran agrees, adding that you need to start thinking about IT/OT convergence differently as well. You must create more synergies among people and machines, and that requires a new strategy.
Were exactly should RFID, machine vision and other automation technologies be used within the reverse logistics chain? Alex Whiting, founder of MiTEQ, reveals what he’s found to work best.
From multiple substrate applications to automated compliance checks, the team at Cobalt Systems has come up with some interesting techniques to affix and validate labels on almost anything using common technology such as industrial printers and industrial scanners. Hear what they've done (and you can do) to the benefit of manufacturing, warehousing, distribution, and fulfillment operations.
Matt Van Bogart is a 20+ year veteran of the industrial imaging and machine vision market. He is currently responsible for machine vision-focused strategic business development at Zebra and previously drove the global channel strategy for industrial automation at the company.
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