The new Version 23.11 of the standard machine software HALCON by MVTec Software GmbH will be available from November 14 and is accompanied by a webinar. For the first time, it is possible to license MVTec HALCON cloud environments without a hardware dongle. Apart from several new features, existing technologies have been further improved.
Machine Vision in the Cloud
The fact that HALCON can now also be operated fully digitally in the cloud creates numerous new application possibilities. These can be setting up new business models, such as offering machine vision services in the cloud, training deep learning models in a computationally intensive way, or enabling cloud-based CI/CD processes.
MVTec License Server Cloud Ready
With HALCON 23.11, customers have an additional “cloud-ready” variant of the license server at their disposal. This now makes it possible to license HALCON in the environments of commercial cloud providers as well as in enterprise-owned cloud setups without the need for a hardware dongle, solely through a network connection. This means that HALCON can now be easily licensed across all cloud solutions. By using HALCON in the cloud, customers can easily benefit from the new possibilities offered by machine vision in the cloud.
Structured Light 3D Reconstruction
In HALCON 23.11, the structured light model has been enhanced: besides deflectometry, it now also provides precise 3D reconstruction for diffuse surfaces in short cycle times. This enhancement gives users the flexibility to develop their own application-specific 3D reconstruction systems using a pattern projector and a 2D camera. The feature is particularly suitable for applications where precise spatial representations are required. As a result, the technology is suitable for the optimization of manufacturing processes, quality control, and the precise measurement of various surfaces.
In the new HALCON version, customers now have access to “multi-label classification”, a new deep learning method that allows the recognition of multiple different classes on a single image. Such classes can encompass various properties of the objects within the image, for example defect types, color, or structure. In practice, this method can, for instance, reveal the presence of different types of defects in an image, allowing a more detailed classification. Compared to other methods, this deep learning method is faster in processing and the effort for labelling is also lower.
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