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Solving the Challenges of Inline Automated Surface Defect Detection

Jason Biddulph

Micro-Epsilon UK Ltd


With a wealth of experience in the Nuclear, Defence, Automotive and FMCG industries, Jason understands engineering processes. His personal knowledge includes hands-on engineering through to the management of engineering teams. As the UK technical lead for Micro-Epsilon in the 2D/3D sensor group and turnkey system solutions, he is responsible for managing projects from initial enquiry through to test evaluation and the integration/commissioning at the end user’s facility. These systems include inline thickness measurement, dimension control and autonomous 3D surface inspection systems.

Anomaly Detection – Identify Undefined Defects

Simon Hickman

Multipix Imaging


Learn how powerful anomaly detection is the new approach to inspection tasks which until recently required an extensive and exhaustive list of known defects to be classified and defined before the inspection would return successful results.  Traditional software tools used for inspection applications can require lengthy development time and result in restrictive defect detection. With stunning results and ease of use, Anomaly Detection has the ability to redefine the use of machine vision in 1000’s of applications.

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The SMART World of Machine Vision

Neil Sandhu



Todays manufacturing and industrial worlds are getting smarter. The intelligence in automation and what we are asking it to do is becoming a more information based environment. Robotic automation is more widely used and on the increase and, what used to be called, the information superhighway is now becoming ‘Industry 4.0’, ‘Internet of Things’ and ‘Big Data’. Here we will explore how todays Vision technology can be incorporated into a ‘smart’ world and environment where the ability that Machine Vision can give to a Robot to see, is one part of the story. Carrying out the Inspection tasks, communication with the Robot but also how the progress, results and conclusions of the entire system can then be tracked, viewed and actioned upon is where the added benefit can be seen. We will look how this can be made possible.


The Machine Vision Conference 2020 presentations housed on this web-page remain the copyright of the originator. PPMA Limited makes no claims, promises or guarantees about the accuracy, completeness or adequacy within each presentation and expressly disclaims liability for any errors, inaccuracies and omissions of its contents. Machine Vision Conference 2020 is a trading style of PPMA Limited, incorporated within England and Wales with company no. 02116954

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