HALCON

MVTec HALCON is the comprehensive standard software for machine vision with an integrated development environment (HDevelop) that is used worldwide. It enables cost savings and improved time to market. HALCON’s flexible architecture facilitates rapid development of any kind of machine vision application.

MVTec HALCON provides outstanding performance and a comprehensive support of multi-core platforms and special instruction sets like AVX2 or NEON, as well as GPU acceleration. It serves all industries (including the industrial internet of things), with a library used in hundreds of thousands of installations in all areas of imaging like blob analysis, morphology, matching, measuring, identification, and 3D vision.

The software secures your investment by supporting a wide range of operating systems and providing interfaces to hundreds of industrial cameras and frame grabbers, in particular by supporting standards like GenICam, GigE Vision, and USB3 Vision.

6
Yes
None
1
1000000
Name...
/brands/halcon/
Thumbnail

Categories:

Sub-Categories:

HALCON 19.05 Progress
HALCON 19.05 Progress Image

View Fullscreen

Please follow and like us:
HALCON 18.11.0.1 Progress
HALCON 18.11.0.1 Progress Image

Features

Deep Learning 

With HALCON 18.11, users are able to train their own classifier using pretrained CNNs (Convolutional Neural Networks) included in HALCON. These networks have been highly optimized for industrial applications and are based on hundreds of thousands of images. HALCON 18.11 offers a seamlessly integrated, comprehensive set of deep learning functions for

  • classifying entire images
  • object detection
  • semantic segmentation

Deep-learning-based image classification allows to easily assign images to trained classes. The low labelling effort enables particularly short set-up times, and applying the classifier to new data is especially fast.

With semantic segmentation, trained defect classes can be localized with pixel accuracy. This allows users to, e.g., solve inspection tasks, which previously could not be realized, or only with significant programming effort.

Object detection localizes trained object classes and identifies them with a surrounding rectangle (bounding box). Touching or partially overlapping objects are also separated, enabling object counting.

To maximize its potential in industrial environments, HALCON’s deep-learning-based image classification, semantic segmentation, and object detection can be performed on GPUs, as well as on x86 CPUs.

 

ECC 200 Code Reader Improvements

With HALCON 18.11, the data code reader for ECC 200 codes has been improved. The overall recognition rate could be increased by 5 % (data based on our internal ECC 200 benchmark consisting of more than 3,700 images from various applications). In addition, the ECC 200 reader is able to read codes with disturbed quiet zone now. Moreover, codes against complex backgrounds can be found and read faster and more robustly.

 

Improved Bar Code Reader

HALCON now features optimized edge detection, which improves the ability to reliably read barcodes with very small line widths as well as strongly blurred codes. Moreover, the quality of the barcodes is also verified in accordance with the most recent version of the ISO/IEC 15416 standard.

 

Improved Automatic Text Reader

HALCON features an improved version of the automatic text reader, which now detects and separates touching characters more robustly.

 

New Data Structure “Dictionaries”

HALCON 18.11 introduces a new data structure “dictionary”, which is an associative array that opens up various new ways to work with complex data.

For example, this allows bundling various complex data types (e.g., an image, cor­re­spond­ing ROIs and parameters) into a single dictionary, making it easier to structure programs when, e.g., passing many parameters to a procedure.

Dictionaries can also be read from and written to a file. This allows an engineer to bundle all information necessary to reproduce a certain application’s state (e.g., camera calibration settings, defective images, and machine parameters) into a single file. This file can then easily be shared with a machine vision expert for offline debugging.

 

Handle Variable Inspect in HDevelop

With HALCON 18.11, HDevelop can display detailed information on most important handle variables.

This allows developers to easily inspect the current properties of complex data structures at a glance, which is extremely useful for debugging. Double-clicking a handle variable now returns all para­meters associated with the handle and their current settings. For example, the user can now easily examine parameters of a data code handle, such as “polarity”, “symbol type” or “finder pattern tolerance”, as well as complex parameters that carry multiple key-value pairs, like for example the camera parameter of a 3D shape model handle.

 

HDevEngine Improvements

HDevelop provides a new library export that makes the use of HALCON procedures from C++ and .NET as easy and intuitive as calling any other C++/.NET function. This is possible via a wrapper that encapsulates the necessary HDevEngine API calls. This new library export also generates CMake projects which can easily be configured to output project files for many popular IDEs, such as Visual Studio. The new functionality is accessible from the HDevelop GUI and via command line interface.

 

Automatic Handle Clearing

HALCON now makes it much more comfortable to work with handles by clearing these automatically once they are no longer required. This significantly reduces the risk of creating memory leaks and makes writing “safe code” much simpler.

 

Surface Fusion For Multiple 3D Point Clouds

HALCON now offers a method that fuses multiple 3D point clouds into one watertight surface. This new method is able to combine data from various 3D sensors, even from different types like a stereo camera, a time of flight camera, and fringe projection. This technology is especially useful for reverse engineering.

 

3D Improvements

HALCON now offers optimized functions for surface-based 3D matching. These can be used to determine the position of objects in 3D space more reliably, making the development of 3D applications easier. In addition, HALCON now also includes a new helper procedure that allows developers to quickly inspect and debug parameters and results of a surface-based matching application.

 

Support for Hypercentric Lenses

A new camera model within HALCON now allows the corrections of distortions in images that were recorded with hypermetric (also known as pericentric) camera lenses. These lenses can depict several sides of an object simultaneously, thus enabling a convergent view of the test object. With this technology, users only need a single camera system for inspection and identification tasks, e.g., the inspection of cylindrical objects.

Deflectometry

HALCON 18.11 includes new operators, which enable the user to inspect specular and partially specular surfaces to detect defects by applying the principle of deflectometry. This method uses the reflections on specular objects’ surfaces by observing mirror images of known patterns and their deformations on the surface.

 

HALCON in Your Industrial Network

HALCON 18.11 introduces the Hilscher-cifX interface. This allows HALCON to communicate with almost all industrial field bus protocols via Hilscher cards. Among others, CC-Link, EtherCAT, EtherNet/IP, PROFIBUS, and PROFINET are supported.
Please follow and like us:
Please follow and like us:
HALCON 18.11.1.1 Steady
HALCON 18.11.1.1 Steady Image

Features

Deep Learning 

With HALCON 18.11, users are able to train their own classifier using pretrained CNNs (Convolutional Neural Networks) included in HALCON. These networks have been highly optimized for industrial applications and are based on hundreds of thousands of images. HALCON 18.11 offers a seamlessly integrated, comprehensive set of deep learning functions for

  • classifying entire images
  • object detection
  • semantic segmentation

Deep-learning-based image classification allows to easily assign images to trained classes. The low labelling effort enables particularly short set-up times, and applying the classifier to new data is especially fast.

With semantic segmentation, trained defect classes can be localized with pixel accuracy. This allows users to, e.g., solve inspection tasks, which previously could not be realized, or only with significant programming effort.

Object detection localizes trained object classes and identifies them with a surrounding rectangle (bounding box). Touching or partially overlapping objects are also separated, enabling object counting.

To maximize its potential in industrial environments, HALCON’s deep-learning-based image classification, semantic segmentation, and object detection can be performed on GPUs, as well as on x86 CPUs.

 

ECC 200 Code Reader Improvements

With HALCON 18.11, the data code reader for ECC 200 codes has been improved. The overall recognition rate could be increased by 5 % (data based on our internal ECC 200 benchmark consisting of more than 3,700 images from various applications). In addition, the ECC 200 reader is able to read codes with disturbed quiet zone now. Moreover, codes against complex backgrounds can be found and read faster and more robustly.

 

Improved Bar Code Reader

HALCON now features optimized edge detection, which improves the ability to reliably read barcodes with very small line widths as well as strongly blurred codes. Moreover, the quality of the barcodes is also verified in accordance with the most recent version of the ISO/IEC 15416 standard.

 

Improved Automatic Text Reader

HALCON features an improved version of the automatic text reader, which now detects and separates touching characters more robustly.

 

New Data Structure “Dictionaries”

HALCON 18.11 introduces a new data structure “dictionary”, which is an associative array that opens up various new ways to work with complex data.

For example, this allows bundling various complex data types (e.g., an image, cor­re­spond­ing ROIs and parameters) into a single dictionary, making it easier to structure programs when, e.g., passing many parameters to a procedure.

Dictionaries can also be read from and written to a file. This allows an engineer to bundle all information necessary to reproduce a certain application’s state (e.g., camera calibration settings, defective images, and machine parameters) into a single file. This file can then easily be shared with a machine vision expert for offline debugging.

 

Handle Variable Inspect in HDevelop

With HALCON 18.11, HDevelop can display detailed information on most important handle variables.

This allows developers to easily inspect the current properties of complex data structures at a glance, which is extremely useful for debugging. Double-clicking a handle variable now returns all para­meters associated with the handle and their current settings. For example, the user can now easily examine parameters of a data code handle, such as “polarity”, “symbol type” or “finder pattern tolerance”, as well as complex parameters that carry multiple key-value pairs, like for example the camera parameter of a 3D shape model handle.

 

HDevEngine Improvements

HDevelop provides a new library export that makes the use of HALCON procedures from C++ and .NET as easy and intuitive as calling any other C++/.NET function. This is possible via a wrapper that encapsulates the necessary HDevEngine API calls. This new library export also generates CMake projects which can easily be configured to output project files for many popular IDEs, such as Visual Studio. The new functionality is accessible from the HDevelop GUI and via command line interface.

 

Automatic Handle Clearing

HALCON now makes it much more comfortable to work with handles by clearing these automatically once they are no longer required. This significantly reduces the risk of creating memory leaks and makes writing “safe code” much simpler.

 

Surface Fusion For Multiple 3D Point Clouds

HALCON now offers a method that fuses multiple 3D point clouds into one watertight surface. This new method is able to combine data from various 3D sensors, even from different types like a stereo camera, a time of flight camera, and fringe projection. This technology is especially useful for reverse engineering.

 

3D Improvements

HALCON now offers optimized functions for surface-based 3D matching. These can be used to determine the position of objects in 3D space more reliably, making the development of 3D applications easier. In addition, HALCON now also includes a new helper procedure that allows developers to quickly inspect and debug parameters and results of a surface-based matching application.

 

Support for Hypercentric Lenses

A new camera model within HALCON now allows the corrections of distortions in images that were recorded with hypermetric (also known as pericentric) camera lenses. These lenses can depict several sides of an object simultaneously, thus enabling a convergent view of the test object. With this technology, users only need a single camera system for inspection and identification tasks, e.g., the inspection of cylindrical objects.

Deflectometry

HALCON 18.11 includes new operators, which enable the user to inspect specular and partially specular surfaces to detect defects by applying the principle of deflectometry. This method uses the reflections on specular objects’ surfaces by observing mirror images of known patterns and their deformations on the surface.

 

HALCON in Your Industrial Network

HALCON 18.11 introduces the Hilscher-cifX interface. This allows HALCON to communicate with almost all industrial field bus protocols via Hilscher cards. Among others, CC-Link, EtherCAT, EtherNet/IP, PROFIBUS, and PROFINET are supported.
Please follow and like us:
HALCON 13
HALCON 13 Image

HALCON 13 – The power of machine vision

Let HALCON 13 excite you with numerous new features and improvements!

 Ready-to-go for Arm-based platforms

HALCON for ARM-based Platforms

With HALCON 13.0.1, MVTec addresses the needs of the rapidly growing embedded vision market – by supporting Arm®-based platforms as a standard, without the need for customized porting. By this, MVTec enables customers to place their image processing algorithms on a wide range of devices with little effort. Learn more about HALCON for Arm-based platforms here.

Speedups

Speedup

With HALCON 13, a giant leap in performance for shape-based matching, one of HALCON’s core technologies, has been accomplished. For example, speedups of more than 300% can be achieved on machines with AVX2-compatible processors, when searching byte images with a small number of pyramid levels. But not only that, HALCON 13 also offers significant speedups for all related technologies, i.e., shape-based 3D matching, local and perspective deformable matching, and component-based matching.

Texture Inspection

Flawless texture inspection training image
Training image with flawless texture
Image displaying a detected defect
Image with a detected defect

 

Texture inspection can be a challenging task because textures often have very different characteristics like scale or bright­ness. Thus, setting up a texture inspection system is often tricky. HALCON 13 therefore offers an easy-to-use texture inspection, which is configured by simply passing some training images. The algorithm automatically adjusts the necessary parameters based on training images that show flawless texture. The trained texture inspection model can then be used to detect potential texture defects.

3D Matching and 3D Reconstruction

Reconstruction of 3D objects
Reconstruction of a 3D object

In HALCON 13, surface-based 3D matching has been improved to be more robust when dealing with flat surfaces. This improvement particularly supports applications like picking of boxes. HALCON 13 also offers a new method to reconstruct 3D objects from multiple cameras with high quality. This new method uses the information of all camera views at once leading to more robust results than provided by common stereo reconstruction methods.

Major improvements in identification technologies

HALCON 13 reading a defective bar code
Automatic text reading of dot print characters

With HALCON 13, MVTec offers deep-learning-based OCR for the first time: HALCON now contains a new OCR classifier based on deep learning (resp. machine learning) technology, which can be used via a number of pretrained fonts. With these, it is possible to achieve higher reading rates than with all previous classification methods. Further, the automatic text reader in HALCON 13 is faster and now also supports reading of dot print characters.

HALCON 13 also reads bar codes even if large parts of the code are either defective or not visible at all. Additionally, the QR code reader has been improved and is now much more robust against common challenges like blur or distortion.

Debugging of HDevEngine applications

Debugging of HDevEngine Applications
Debugging of HDevEngine Applications

With HALCON 13, HDevEngine applications can now be de­bugged directly within HDevelop. HDevEngine allows de­velopers to execute HDevelop code within their C# or C++ application. By attaching HDevelop to this application, the machine vision part can now be debugged using HDevelop. This debugging enables the developer to inspect call stack and variable values while executing procedures step by step, making error tracking a lot easier.

You can even connect HDevelop to an HDevEngine application running on a different computer for remote debugging. For example, debugging a machine on the factory floor can be done directly from the office with this connection. This functionality is also extremely useful for debugging HALCON Embedded devices such as smart cameras.

 

View Fullscreen

 

HALCON 18.11.0.1 Progress Image
HALCON 18.11.0.1 Progress
Features Deep Learning  With HALCON 18.11, users are able to train their own classifier using pretrained CNNs (Convolutional Neural ... Read More
HALCON 13 Image
HALCON 13
HALCON 13 – The power of machine vision Let HALCON 13 excite you wit... Read More