| HALCON, MERLIC, Trade Fair, Americas, Canada

CVPR 2023

Visit the MVTec booth at CVPR 2023 in Vancouver, Canada, and discover the latest innovations in machine vision.

We are pleased to announce that MVTec will be an exhibitor at CVPR 2023, the leading conference for computer vision and pattern recognition!

From June 19-23, 2023, you will have the opportunity to visit us at our booth and learn about our latest innovations and products. CVPR is a unique opportunity for us to network with other industry experts and discuss the latest trends and developments in machine vision.

In a live demonstration, we show you how the easy-to-use machine vision software MERLIC inspects electronic components on printed circuit boards using the new Global Context Anomaly Detection technology. In this process, logical anomalies are detected - i.e., both local, minor defects such as scratches as well as large-scale logical errors such as misplaced labels. The technology introduced in the MERLIC 5.2 release can be used for completeness checks and defect detection as part of quality control.

Visit us at boot 1026 and experience for yourself how our technology and know-how can help you realize your vision!

Paul Bergmann from MVTec Research is also part of the organizing team of the "VAND: Visiual Anomaly and Novelty Detection" Workshop:

Anomaly detection, and thesynonymous topics of novelty and out-of-distribution detection, represent an important and application-relevant challenge within both computer vision and the broader fieldof pattern recognition. In its simplest formulation, anomalydetection targets the identification of samples which deviate froman obtained approximation to the true distribution of normality for agiven dataset. As such anomalies represent unexpected eventualitiesor outliers in the scope of a given task. The notion of detecting themeffectively and efficiently has been sought after for many real-worldapplications including medical diagnosis, airport security screening,industrial inspection, or crowd control. We now see the rise of a complex and vibrant set of learning-based paradigms addressing the anomaly detection task - varying acrossboth the fully/semi/un-supervised and few/one/zero shot axes ofrecent computer vision and pattern recognition research. Thisworkshop brings together researchers of both industry andacademia to present and discussrecent developments, opportunitiesand open challenges in this area. The workshop will also host achallenge for zero-/few-normal-shot anomaly detection, toencourage the development and benchmarking new algorithms forrealistic yet challenging tasks.