![](/uploads/1/2/6/6/126629689/905836393.jpg)
To try out our machine vision software MVTec HALCON for free, please follow the instructions below. Please register for our Customer Area. If you are already registered, you can directly go to the login.
MVTec HALCON provides outstanding performance and comprehensive support of multi-core platforms, special instruction sets like AVX2 and NEON, as well as GPU acceleration. With a library that covers all areas of imaging like blob analysis, morphology, matching, measuring, and identification. Sep 10, 2015 It’s really easy to create a machine vision application with the MVTec software MERLIC. In this video, we will show you how to create a simple application that checks whether all fuses are.
Within the Customer Area you can download the latest versions of HALCON and our other products.Here, you also have the option to request an application evaluation for free. Just describe your task and see how HALCON can efficiently solve it. Before testing HALCON you need to obtain an evaluation license, which is valid for 30 days. To get such a license, please contact one of the.
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.MVTec HALCON provides outstanding performance and comprehensive support of multi-core platforms, special instruction sets like AVX2 and NEON, as well as GPU acceleration. With a library that covers all areas of imaging like blob analysis, morphology, matching, measuring, and identification. The software provides the latest state-of-the-art machine vision technologies, such as comprehensive 3D vision and deep learning algorithms.
In HALCON 19.05 the deep learning functionality can be applied to a broader range of applications. Please note that this update is only available with the progressive license model.Enhanced Object detectionUsers now have the option of aligning object detection boxes with the orientation of the object, enhancing the localised trained object classes.Inference on ARM ProcessorsInference for all three deep learning technologies – image classification, object detection, and semantic segmentation – runs out-of-the-box on Arm® processors. As this removes the need for special components like a powerful GPU or a desktop CPU, HALCON significantly broadens the range of possible deep learning applications.
Execution times on Arm®-based platforms vary by complexity and the type of hardware, but MVTec benchmarks have shown them to be suitable for many conceivable applications.Shape Based MatchingShape-based matching is one of HALCON's most important core technologies and can be considered to be one of the most powerful matching tools on the market. MVTec continuously improves this technology to widen the application area even further.
With HALCON 19.05, users can now, for example, specifically define so-called 'clutter' regions (markedabove in orange). These are areas within a search model that should not contain any contours.Surface Based MatchingEdge-supported surface-based matching is now more robust against noisy point clouds: Users can control the impact of surface and edge information via multiple min-scores. Additionally, in case that no xyz-images are available, a new parameter now allows switching off 3D edge alignment entirely. This enables users to eliminate the influence of insufficient 3D data on matching results, while keeping the valuable 2D information for surface and 2D edge alignment.SpeedupsVarious operators in HALCON have been sped up.
For example, depending on image type and settings, affinetransimage is now up to 230% faster onAVX2 processors. Furthermore, polartransimageext can be executed up to160% faster, depending on the interpolation method.
HALCON 18.11 will be available in two editions: Steady and Progress. While the latter is available as a subscription with a six-month release cycle, the Steady edition – as successor of HALCON 13 – is offered for regular purchase.Deep LearningThe new version 18.11 further expands the deep learning capabilities of HALCON. Even more powerful deep learning algorithms now allow users to locate objects within an image with pixel-precision or bounding box accuracy. There is no need to startfrom scratch, as our standard machine vision software comes with various pretrained Convolutional Neural Networks (CNNs), that have been highly optimized for industrial applications.Semantic SegmentationWith HALCON 18.11, object- or error classes trained with deep learning can now be segmented pixel-precisely. Combined with the multitude of possibilities that HALCON offers for further processing extracted regions, this paves the way for an entirely new range of applications, which previously could not be realized,or only with significant programming effort.
For example: recognising objects with a very heterogeneous texture (e.g., plants) or detecting defects on various types of objects and materials, e.g., pills, glass or leather.Object DetectionHALCON 18.11 introduces deep-learning-based object detection, which allows customers to localise trained object- or error classes in an image. In contrast to semantic segmentation, objects are marked by a surrounding rectangle (bounding box).
The object detection also separates instances of the same class, even if the objects touch each other or partially overlap. This is especially useful when the exact amount of objects is needed, e.g., when checking pill bags for correct filling.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. It is possible, for example, to bundle various complex data types (e.g., an image, corresponding ROIs, and parameters) into a single dictionary: This helps to structure programs when, e.g., passing many parameters to a procedure.Handle Variable Inspect in HDevelopWith 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.ECC 200 Code Reader ImprovementsThe data code reader for ECC 200 codes has been improved.HALCON in Your Industrial NetworkHALCON 18.11 introduces the Hilscher-cifX interface.
This allows HALCON to communicate with almost all industrial field bus protocols via Hilscher cards.Improved Barcode ReaderHALCON 18.05 features optimized edge detection, which improves the ability to reliably read bar codes with very small line widths as well as strongly blurred codes. Moreover, the quality of the bar codes is also verified in accordance with the most recent version of the ISO/IEC 15416 standard.Enhanced DeflectometryThe deflectometry functionality introduced in HALCON 17.12 now includes a new pattern type that improves the precision and robustness of error detection especially on partially specular surfaces like varnished metal sheets.3D ImprovementsHALCON 18.05 offers optimized functions for surface-based 3D matching. These can be used to determine the position of objects in 3D space more reliably, making 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.Automatic Handle ClearingHALCON 18.05 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 because you no longer have to manually release unused memory.
This way, writing 'safe code' is now much simpler.HDevEngine improvementsThe HDevelop library export feature has been expanded: Developers can now access HDevelop procedures not just in C, but also in.NET via an exported wrapper – as easily and intuitively as a native function. This significantly facilitates the development process.Support for Hypercentric LensesA new camera model within HALCON now allows the corrections of distortions in images that were recorded with hypercentric (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.Deep LearningWith MVTec HALCON 17.12, you are able to train your own CNN classifier (Convolutional Neural Network). After training the CNN, the network can be usedto classify new data with HALCON.DeflectometryIn order to address the special challenges imposed by inspecting specular reflecting surfaces for defects like dents and scratches, HALCON nowenables you to apply the principle of deflectometry. This method uses specular reflections by observing mirror images of known patterns and theirdeformations on the surface.
Automatic text readerHALCON 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 cloudsHALCON now offers a new method that fuses multiple 3D point clouds into one watertight surface. This new method is able to combine data fromvarious 3D sensors, even from different types like a stereo camera, a time of flight camera, and fringe projection. This technology is especiallyuseful for reverse engineering.
HDevEngine improvementsWith the new HDevelop library export included in HALCON 17.12, calling HDevelop procedures from C is as easy and intuitive as calling anyother C function. This new library export also generates CMake projects, which can easily be configured to output project files for manypopular IDEs, such as Visual Studio.SpeedupsWith 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 inspectionTexture 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 reconstructionIn 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 technologiesWith 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.
![](/uploads/1/2/6/6/126629689/905836393.jpg)