Machine Vision Software Offers Global Context Anomaly Detection
HALCON`s Deep OCR enables users to efficiently solve text reading applications in a multitude of use cases. With the latest HALCON 22.05 machine vision software, the technology is extended by training functionality, enabling application specific training on the user’s own application dataset. This allows you to solve even most complex applications like reading text with bad contrast (e.g., on tires). Another advantage is that very rarely used special characters or printing styles can also be trained. Training for Deep OCR significantly improves the performance and usability and makes applications run even more robust.
Global Context Anomaly Detection
HALCON 22.05 opens up completely new application possibilities with the detection of logical anomalies in images. This is the further development of the deep learning technology anomaly detection. Until now, it was possible to detect local, structural anomalies. The new ‘Global Context Anomaly Detection’ is a one-of-a-kind technology, which is able to ‘understand’ the logical content of the entire image. Just like HALCON`s existing anomaly detection the new Global Context Anomaly Detection only requires ‘good images’ for training, eliminating the need of data labeling.
This technology makes it possible to detect entirely new variants of anomalies like missing, deformed, or incorrectly arranged components. It opens up completely new possibilities: For example, the inspection of printed circuit boards in the semiconductor production or the inspection of imprints.
Quality Inspection of ECC 200 Codes
Print Quality Inspection (PQI) refers to the evaluation and grading of certain aspects of printed bar and data codes according to international standards. For example, it indicates how reliable a code can be read by various code readers or how stable the print quality is in a manufacturing process. HALCON supports various standards for grading the print quality of 1D and 2D codes. With HALCON 22.05, the PQI of data codes has been further improved. It is now up to 150% faster. In addition, the module grid determination for print quality inspection of ECC 200 has been improved. Last but not least, the usability of the PQI of data codes has been improved by introducing a new procedure that provides the grades.
With HALCON 22.05, various improvements are released. One example is a new operator that performs adaptive histogram equalization to improve contrast locally in an image. This helps to extract significantly more information from images with low contrast, especially in case of inhomogeneous gray value gradient. Besides, the HALCON library has been extended with a new operator which allows image smoothing with arbitrarily shaped regions. Furthermore, another new operator allows users to transform 3D points using a rigid 3D transformation that is specified as a dual quaternion. HDevelop’s Matching Assistant also now generates the code based on Generic Shape Matching.
For more information: www.mvtec.com