HALCON 21.05 Progress: Quick Guide to Features & Improvements
Published on May 21, 2021 by TIS Marketing.
MVTec has just released their latest HALCON Progress Edition: 21.05. Below are the new features and improvements:
- Optimized Subpixel Bar Code Reader
- Deep OCR Improvements
- HDevelop Usability Improvements
- Generic Shape Matching
- HALCON Deep Learning Framework
- Improvements of Basic 2D and 3D Opereators
HALCON's Subpixel bar code readers for low-resolution bar codes: Logistics is experiencing increasing growth in the number of camera-based identification systems. On the warehouse floor, however, there is high-variance in the hardware and other environmental conditions which can lead to less-than-optimal bar code images. HALCON can now read bar codes with even minimal module size. HALCON's improved functionality can increase the decoding rate by up to 50%.
Deep OCR improvements - A holistic deep-learning-based approach: Compared to other algorithms, Deep OCR can localize characters much more robustly - regardless of orientation, font type or polarity. With HALCON 21.05, big images are now handled more robustly, and words of varying lengths - even very long words - can now be detected without parameter tuning. Thanks to the extended set of supported characters in HALCON 21.05, customers also benefit from overall stability improvements and a wider range of possible applications.
HDevelop usability improvements: HDevelop's user interface has also been made more user friendly. The new "Canvas Window" allows users to group and organize graphics windows in a much more convenient manner. Additionally, users now have more options to control and dock the position of floating windows. The new "Auto-hide" feature enables users to quickly shrink unused widgets into the sidebar and easily bring them back when needed.
Generic shape matching: HALCON 21.05 introduces Generic Shape Matching to enhance the usability of MVTec's industry-proven shape matching technologies. By significantly reducing the number of required operators, users can now implement their solution much more quickly and easily. With the new release, it is now possible to strictly delimit the angle and scaling range. Moreover, thanks to the unification of the different shape matching methods into a single set of operators, users can now more efficiently adapt new shape-matching features.
HALCON Deep Learning Framework: With HALCON 21.05, MVTec introduces the first version of the HALCON Deep Learning Framework. This framework allows experienced users to create their own networks within HALCON. With this feature, experts can now realize even the most demanding and highly complex applications in HALCON without having to rely on pretrained models or third-party frameworks.
Improvements of basic 2D and 3D opereators: In HALCON 21.05, 3D-point-cloud sampling supports a new mode called "furthest point" which usually results in a more even sampling of the 3D object. Point cloud smoothing has also been added with the release--a feature which typically significantly improves processing times. Other improvements in HALCON 21.05 include general speedups of basic image operators.
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