HALCON is a comprehensive standard software for machine vision with an integrated, highly interactive development environment (IDE) which allows concurrency thanks to the support of parallel programming. Event-based processing is supported. Debugging tasks are very easy with direct inspection of HALCON variables (tuples and iconic) in Visual Studio.
Features of HALCON Progress Edition (HALCON 17.12)
With MVTec HALCON 17.12, you are able to train your own CNN classifier (Convolutional Neural Network). After training the CNN, the network can be used
to classify new data with HALCON.
In order to address the special challenges imposed by inspecting specular reflecting surfaces for defects like dents and scratches, HALCON now
enables you to apply the principle of deflectometry. This method uses specular reflections by observing mirror images of known patterns and their
deformations on the surface.
Automatic text reader
HALCON 17.12 features an improved version of the automatic text reader, which now detects and separates touching characters more robustly.
Surface fusion for multiple 3D point clouds
HALCON now offers a new 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.
With the new HDevelop library export included in HALCON 17.12, calling HDevelop procedures from C++ is as easy and intuitive as calling any
other C++ function. 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
Features of HALCON Steady Edition (HALCON 13)*
With HALCON 13, a giant leap in performance for shape-based matching, one of HALCON's core technologies, has been accomplished. 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 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
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
With HALCON 13, MVTec offers deep-learning-based OCR for the first time: HALCON now contains a new OCR classifier and comes with a number of pre-trained fonts based on deep learning technology. 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.
The first HALCON Steady Edition is scheduled for release by the end of 2018. Until then, customers who are interested in HALCON Steady can purchase HALCON 13 and benefit from the same advantages.