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Introduction to Machine Vision

Learning the basics of machine vision can help simplify inspection applications and make results more reliable and repeatable on your production line. Machine vision solutions can help you automate inspections, increase throughput and improve product quality. If you are looking for a better understanding of machine vision technology and how it works, then this webinar is for you!




Originally presented: May 2, 2024
Duration: 1 hour
Presented by:

Overview

Machine vision is an essential component of how digital systems interact with the real world. It lets automated systems see components, products, patterns, codes, or other objects and use that information to make decisions.

Since it allows manufactured parts and products to be inspected, measured, and sorted, machine vision has vastly increased the power and flexibility of industrial automation. Machine vision does all this at high speeds and high accuracy, improving product quality and reducing waste.

Machine vision also lets automated equipment locate objects, identify them, and save information about their material, condition, orientation, and other details for later analysis. That data is critical to factories looking for efficiency gains.

Check out this webinar to brush up on the basics of machine vision and edge learning.

Key Takeaways

  • Discover how machine vision increases the power and flexibility of industrial automation.
  • Explore improvements in inspection applications and production line efficiency associated with machine vision technology.
  • Learn how new AI technology can simplify the process.
  • Understand how machine vision also lets automated equipment locate objects, identify them, and save information about their material, condition and other details for later analysis.

 

Speaker

Mary Wang, Product Marketing Specialist, Cognex Corporation

Wang has been with Cognex for over a year and is working on the In-Sight 2D team helping with the In-Sight SnAPP product launch. She holds a bachelor’s degree in applied mathematics from University of Michigan, Ann Arbor, and a master’s degree in business management from Brown University.