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This allows HALCON to communicate with almost all industrial field bus protocols via Hilscher PC cards. HALCON 18.11 introduces the Hilscher-cifX interface.

ATC4 RELEASED INSTALL

The Linux installer, which can be downloaded from the MVTec website, has been extended by the option to additionally install the components necessary for 64-bit Arm-based platforms. HALCON now also supports 64-bit ArmВ®-based platforms out of the box.

Double-clicking a handle variable now returns all paraВmeters associated with the handle and their current settings. This allows developers to easily inspect the current properties of complex data structures at a glance, which is extremely useful for debugging. HDevelop can now display detailed information on most important handle variables.

Dictionaries can also be read from and written to a file. This enables developers to bundle arbitrary data into a single variable, making it easier to structure complex procedures. HALCON now includes a new data structure 'dictionary', which is an associative array that opens up various new ways to work with complex data. Also, codes against complex backgrounds can now be read faster and more robustly. In addition, the ECC 200 reader is now able to read codes with a disturbed quiet zone.

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The overall recognition rate of the ECC 200 data code reader could be increased by 5 % (data based on our internal benchmark consisting of more than 3,700 images from various applications). This is especially useful when objects need to be counted. Object detection also separates instances of touching or partially overlapping objects. Object detection localizes trained object classes and identifies them with a surrounding rectangle (bounding box). This allows users to, e.g., solve inspection tasks, which previously could not be realized, or only with significant programming effort. With semantic segmentation, trained defect classes can be localized with pixel accuracy. Combined with the preexisting deep-learning-based image classification, users now have a comprehensive set of deep learning functions at their disposal. HALCON 18.11 Progress introduces two new deep learning functionalities: object detection and semantic segmentation. Major New Features of HALCON 18.11.0.0 Progress

Release Notes of Previous HALCON Versions.

Detailed Description of Changes in HALCON 18.11.0.0 Progress.

ATC4 RELEASED WINDOWS

Planned Discontinuation of the x86-win32 platform version for Windows.

Legacy or No Longer Supported Functionality.

Major New Features of HALCON 18.11.0.0 Progress.

Under Windows, by inspecting the file properties of the files The file version of the HALCON library can also beД¬hecked with the following operator call: get_system('file_version',FileVersion), or, HALCON library is indicated by: 'HALCON version: 18.11.0.1 To find out which version is currently installed, please open

Substituted by an updated version HALCON 18.11.0.1 Progress. Therefore, the original version HALCON 18.11.0.0 Progress was For script code that mostly manipulatedД¬ontrol variables this could produce an overhead of about 15%. There was a performance regression in HDevEngine execution Now, new threads are only started when they are actually Led to a substantial resource consumption. When many HDevProcedure instances were created, this Separate thread by default (besides the engine's own mainД®xecution thread), regardless whether that thread was used later The originally released version of HALCON 18.11.0.0 Progress had a few issues:ДЇor each instance of an HDevProcedure, HDevEngine has started a This document provides the release notes for MVTec HALCON 18.11.0.1 Progress, as released in December 2018.ДЄddendum to the Release Notes of HALCON 18.11.0.0 Progress

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