MVTec HALCON 2024 v24.11 full crack license

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MVTec HALCON 2024 v24.11 full crack license

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Machine vision software becomes even more powerful and user-friendly: MVTec presents HALCON 24.11
Users can look forward to groundbreaking technologies and improvements. “With HALCON 24.11, we are focusing on even better AI, specifically deep learning algorithms. Among other features, users can now detect and evaluate unexpected behavior in deep-learning-based classification. Many customers will also appreciate the optimized QR code reader, which is now even more powerful, particularly in challenging conditions,” explains Jan Gärtner, Product Manager for HALCON at MVTec. The shape-based matching technology has also been made more user-friendly, making it accessible to users without advanced machine vision expertise. Furthermore, the HALCON software editions licensing model has been improved: users of the HALCON Progress Edition can now work on HALCON Steady projects without restrictions. From version 24.11 onwards, the editions are fully compatible with each other. Additionally, the new version offers developers a preview of HALCON’s new integrated development environment, HDevelopEVO.

Out of Distribution Detection (OOD) for Classification
With this new HALCON feature, unexpected behavior caused by incorrect classifications in production can be easily detected, and targeted actions, such as stopping the machine, can be efficiently initiated. When using a deep learning classifier, unknown objects are assigned to one of the classes that the system has learned. This can lead to problems if, for example, the defects or objects themselves are of a type that has never occurred before. The new deep learning feature “Out of Distribution Detection (OOD)” notifies users when an object is classified that was not included in the training data. For instance, this could be a bottle with a green label when the system was only trained on bottles with red or yellow labels. In such cases, HALCON provides the message “Out of Distribution” along with an OOD score, indicating the degree of deviation from the trained classes.
The OOD score can also be useful when expanding deep learning models with new training images by indicating which of the new images will have the greatest value for the new model. For example, a high OOD score for a new training image indicates a greater deviation from the images already in the network, meaning higher information content and, therefore, greater value for the training.

Improved Shape-based Matching
The new HALCON version makes the “Shape-based Matching” feature, commonly used in many applications, more user-friendly. This technology is used to locate objects fast, accurately, and precisely. HALCON 24.11 introduces the new patent pending “Extended Parameter Estimation” for this purpose. This allows parameters to be estimated with greater granularity, which significantly speeds up execution in some applications. “Extended Parameter Estimation” enables this estimation also for users without in-depth machine vision expertise.