April 16, 2024 | Press Release | HALCON
With this release, MVTec has focused on improving HALCON’s ease-of-use and rule-based machine vision. The most significant innovation in HALCON 24.05 is the extended parameter estimation for Shape Matching. "Matching methods are the basis of many machine vision applications, as they are used to find the relevant objects in an image with sub-pixel accuracy. With the new development, we have increased the user-friendliness by adding an automated parameter estimation in HALCON. This means that fast and robust solutions can now be developed even without in-depth expert knowledge," explains Jan Gärtner, Product Manager HALCON.
In addition to the extended parameter estimation for Shape Matching, the new release will also provide customers with higher decoding rates for bar code reading and includes various measures to improve core technologies. As of version 24.05, HALCON natively supports the STEP format, the industry standard for 3D CAD data. Along with this release, MVTec will publish an updated version of its OpenVINO™ Toolkit AI² plug-in. "We always strive to provide our customers with the latest technologies. This also means that we constantly improve existing methods. Thus, we were able to accelerate the performance of our deep learning methods with the new OpenVINO Toolkit AI² plug-in, and our customers also benefit from many performance improvements in rule-based algorithms," says Jan Gärtner.
HALCON 24.05 introduces the first iteration of the extended parameter estimation for Shape Matching. With its subpixel accuracy, Shape Matching finds objects robustly and accurately in real-time, even in the most challenging situations. Thanks to the extended parameter estimation, manual parameter adjustments will soon be a thing of the past. Using multiple annotated images, users can now easily optimize for maximum online speed while keeping robustness through automated parameter tuning. Users thus benefit from a faster implementation of shape matching applications, even without specialized expertise.
The bar code reader for GS1 DataBar Expanded Stacked codes has been improved in HALCON 24.05. Depending on the application, customers can expect significant improvements to their decoding rates. This will especially benefit industries such as logistics, retail, and manufacturing, where stacked bar codes are an essential means for tracking and tracing goods.
Starting with version 24.05, HALCON supports the STEP (Standard for the Exchange of Product Data) file format, the industry standard for 3D CAD data. Customers can now seamlessly load STEP CAD data directly into a HALCON 3D object model without any intermediate steps or conversions. The STEP format is supported by most common CAD programs, increasing interoperability and efficiency, because models for 3D matching can be taken directly from the planning data in the CAD software.
Parallel to the HALCON 24.05 release, a new version of the OpenVINO Toolkit AI² plug-in will be released. This update uses the latest LTS version of the Intel® Distribution of OpenVINO™ Toolkit, ensuring compatibility with the latest Intel hardware and boosting the inference performance of deep learning applications. Notably, the new plug-in version enhances support for Intel’s 13th generation of Intel Core processors, leading to improved inference performance. In addition, customers can now also utilize Intel’s discrete graphics cards for inference, providing greater flexibility in selecting the appropriate hardware for their application.
HALCON 24.05 also includes several performance optimizations for various core technologies. For example, unwarping byte images using a vector field is now up to 285 % faster on AVX2-capable Intel CPUs. The operator map_image is now up to 25% faster as well.
In addition, HALCON 24.05 provides adjustments to many operators to address performance impacts resulting from Intel's resolution of the "Downfall" security vulnerability.
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Published on: April 16, 2024
