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Effectiveness of CPU-based deep learning inference for machine vision

Jason MacDonald

Matrox Imaging



This presentation will explore the use of deep leaning technology in machine vision applications. It will specifically explore the effectiveness of CPU-based deep learning inference including the benefits and considerations to choosing that approach.

Choosing the Most Appropriate Deep Learning-based Tool for your Application

Jean-Marie Jolet




In this presentation, we will review classical deep learning-based tools such as classifiers, segmenters (semantic, as well as unsupervised), and objects detectors. Focusing mainly of their advantages and drawbacks, we will explain how these tools meet typical requirements of machine vision applications, and how to choose between them.

Achieving Simple Deep Learning Success

Neil Sandhu




An overview of utilising a method of AI/Deep Learning from initial challenge to solution deployment. The session will look at the types of challenges that the technology can help solve, methods of structuring the teaching process and then how to deploy and continually evolve and optimise.

Anomaly Detection Based on Deep Learning

Dr. Antje Aufderheide




In this video, you can see a detailed demonstration of MVTec’s anomaly detection based on deep learning. MVTec’s deep-learning-based anomaly detection significantly facilitates the automated surface inspection for, e.g., detection and segmentation of defects. The technology is able to unerringly and independently localize deviations, i.e., defects of any type, on subsequent images. You only need a low number of high-quality images for training because defects of varying appearance can be detected without any previous knowledge or any preceding labeling efforts. Training a new network can mostly be done in a matter of seconds, allowing users to perform many iterations to fine-tune their application without sacrificing a lot of precious time.

Ready-to-integrate smart vision solutions

Tim Miller

NeT New Electronic Technology GmbH



NET introduces latest smart vision technologies for a faster ´going smart´. The discussed, freely configurable smart vision solutions reduce complexity and allow a faster time-to-market. Concrete applications examples are given.


The Machine Vision Conference 2021 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 2021 is a trading style of PPMA Limited, incorporated within England and Wales with company no. 02116954

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