| Trade Fair, Americas, USA

Automate 2022

"Technology never stops advancing. Neither should you.": Talk to our machine vision experts and find out more about how MVTec's products can enhance your applications.

A front seat to the future

Automate is produced by the Association for Advancing Automation (A3) and is the largest solutions-based showcase of automation, robotics, vision, motion control and more in North America.

Meet our MVTec experts and learn more about the latest features of MVTec's HALCON standard software, Deep Learning Tool, and MERLIC. To find out more about the latest advancements in bar and data code reading as well as the detection of anomalies make sure you get a closer look at our live demonstrations:

Bar  and Data Code Reading

The complexity and speed requirements of logistics tasks are constantly increasing. Product identification and traceability is an increasingly important necessity for many modern producers. Goods are printed, marked, or engraved with various barcodes, datacodes, characters and symbols to identify contents and provide vital manufacturing information. Bar code and data code reading is one of the easiest ways to identify items, so far. There are several different types of barcode and data code available. Both methods faced basically the same challenges as codes can be damaged or otherwise disturbed. Most recent machine vision algorithms tackle these challenges, enabling robust and fast decoding of the information. The error-free identification of bar codes with our software products is constantly improved. Our showcase demonstrates live that all common bar and data codes can be read with MVTec's software products regardless of orientation - even with an element width of less than 1 pixel (subpixel), or, if the code is partly occluded.

Anomaly Detection with MERLIC

With the help of a live demonstraton, you will be introduced to anomaly detection with MERLIC. Deep-learning-based anomaly detection significantly facilitates the automated surface inspection for, e.g., detection and segmentation of defects. Only a low number of high-quality images is needed for training because defects of varying appearance can be detected without any previous knowledge or any preceding labeling efforts. Learn more about our deep learning technologies.