MVTec
HALCON Steady 22.11
HALCON STEADY 22.11
HALCON 22.11 opens up completely new application possibilities with the detection of logical anomalies in images. This is the further development of the deep learning technology anomaly detection. Until now, it was possible to detect local, structural anomalies. The new "Global Context Anomaly Detection" is a one-of-a-kind technology, which is able to "understand" the logical content of the entire image. Just like HALCON's existing anomaly detection, the new "Global Context Anomaly Detection" only requires "good images" for training, eliminating the need of data labeling. This technology makes it possible to detect entirely new variants of anomalies like missing, deformed, or incorrectly arranged components. It opens up completely new possibilities: For example, the inspection of printed circuit boards in the semiconductor production or the inspection of imprints.
HALCON 22.11 opens up completely new application possibilities with the detection of logical anomalies in images. This is the further development of the deep learning technology anomaly detection. Until now, it was possible to detect local, structural anomalies. The new "Global Context Anomaly Detection" is a one-of-a-kind technology, which is able to "understand" the logical content of the entire image. Just like HALCON's existing anomaly detection, the new "Global Context Anomaly Detection" only requires "good images" for training, eliminating the need of data labeling. This technology makes it possible to detect entirely new variants of anomalies like missing, deformed, or incorrectly arranged components. It opens up completely new possibilities: For example, the inspection of printed circuit boards in the semiconductor production or the inspection of imprints.
Highlights
- HALCON is your solution for the full range of applications in the field of machine vision
- Enabler of the Industrial Internet of Things (aka Industry 4.0)
- Large imaging library of more than 2,100 operators
- Integrated development environment (IDE) for machine vision: HDevelop
- Huge range of features including deep learning
- Easy programming in C, C++, C#, Python, and Visual Basic .NET
- Available for a multitude of platforms
- Support of multi-core and multiprocessor computers
- High performance through utilization of state-of-the-art instruction sets and GPU Acceleration
- Support of hundreds of industrial cameras, frame grabbers, and all common vision standards
Features
- Revolutionary software for 3D machine vision
- Matching to find even rotated or partly occluded objects
- Blob analysis with more than 50 shape and gray value features
- High-accuracy measuring
- Huge range of latest deep learning technologies
- Optical character recognition and verification (OCR/OCV)
- Arbitrarily shaped regions of interest (ROIs) for significant flexibility and speed
- Detection of lines, circles, and ellipses with an accuracy of up to 1/50 pixel
- Extremely fast morphology
- Color image processing and hyperspectral imaging
- Processing of extremely large images (more than 32k x 32k)
- Image sequence processing (e.g., for surveillance tasks)
- Accurate 3D camera calibration
NEWEST HALCON FEATURES
3D GRIPPING POINT DETECTION
HALCON 22.11 combines 3D vision and deep learning for the first time. The 3D Gripping Point Detection can be used to robustly detect surfaces on any object that is suitable for gripping with suction. In contrast to classic bin-picking applications, the 3D Gripping Point Detection is a CAD-less approach, hence no prior knowledge of the respective objects is required. This increased flexibility opens up completely new application fields, such as those in the logistics industry or warehouses.
NEW DATA TYPE “MEMORY BLOCK”
As of HALCON 22.11, users can store and transfer binary data (e.g., images) in HALCON as well as further process it with other applications. This increases the software’s compatibility with machine communication protocols, such as OPC UA or image acquisition interfaces.
PROTECTION OF TRAINED DEEP LEARNING MODELS
For machine vision applications, the protection of intellectual property is getting more and more important. This is particularly relevant in the field of deep learning. The special aspect regarding this technology is that compared to traditional methods, the quality depends not only on the algorithm itself but also significantly on the quality of the training data. A large part of the effort of deep learning applications is in collecting the data and training the models. Therefore, HALCON 22.11 includes a new encryption mechanism for HALCON data types. One major use case is the encryption of deep learning models. This allows customers to protect their investment and know-how. Thanks to this, it is ensured that only authorized users can use and view their deep learning model.
BETTER TRACEABILITY OF DEEP LEARNING DECISIONS
A heatmap gives an indication of which areas of an image were decisive for the result of the deep learning model's classification. This can shed more light into the black box of deep learning, thereby increasing the traceability of corresponding processes. Guided Grad-CAM is a new method that now provides even more precise clues as to which regions of the image are relevant for the decision made by the deep learning network. For example, misclassifications can be investigated more precisely in a post-processing step.
NETWORK LICENSES
With HALCON 22.11, MVTec expands the licensing possibilities by adding the option to license HALCON via a network. A license server allows the use of floating licenses. Here, developers share a predefined number of licenses using a network connection. Customers benefit from cost savings due to multi-usage and greater flexibility in user allocation, developers enjoy greater independence and flexibility regarding their work location. Especially for distributed or remotely working development teams, this is the perfect way to effectively make use of HALCON’s powerful machine vision algorithms. Besides this, the new mechanism enables users to work in virtualized environments without permanent physical host ID.
DEEP OCR TRAINING
HALCON's Deep OCR enables users to efficiently solve text reading applications in a multitude of use cases. With HALCON 22.05, this technology is extended by training functionality, enabling application-specific training on the user's own application dataset. This allows you to solve even most complex applications like reading text with bad contrast (e.g., on tires). Another advantage is that very rarely used special characters or printing styles can also be trained. Training for Deep OCR significantly improves the performance and usability and makes applications run even more robust.
Reading text with bad contrast and lighting conditions on tires without ...
and with the new Deep OCR training.
GLOBAL CONTEXT ANOMALY DETECTION
HALCON 22.05 opens up completely new application possibilities with the detection of logical anomalies in images. This is the further development of the deep learning technology anomaly detection. Until now, it was possible to detect local, structural anomalies. The new “Global Context Anomaly Detection” is a one-of-a-kind technology, which is able to "understand" the logical content of the entire image. Just like HALCON`s existing anomaly detection the new “Global Context Anomaly Detection” only requires "good images" for training, eliminating the need of data labeling.This technology makes it possible to detect entirely new variants of anomalies like missing, deformed, or incorrectly arranged components. It opens up completely new possibilities: For example, the inspection of printed circuit boards in the semiconductor production or the inspection of imprints.
IMPROVED PRINT QUALITY INSPECTION FOR ECC 200 CODES
Print Quality Inspection (PQI) refers to the evaluation and grading of certain aspects of printed bar and data codes according to international standards. For example, it indicates how reliable a code can be read by various code readers or how stable the print quality is in a manufacturing process. HALCON supports various standards for grading the print quality of 1D and 2D codes. With HALCON 22.05, the PQI of data codes has been further improved. It is now up to 150% faster. In addition, the module grid determination for print quality inspection of ECC 200 has been improved. Last but not least, the usability of the PQI of data codes has been improved by introducing a new procedure that provides the grades.
QUALITY-OF-LIFE IMPROVEMENTS AND SPEED-UPS
With HALCON 22.05, various improvements are released. One example is a new operator that performs adaptive histogram equalization to improve contrast locally in an image. This helps to extract significantly more information from images with low contrast, especially in case of inhomogeneous gray value gradient. Besides, the HALCON library has been extended with a new operator which allows image smoothing with arbitrarily shaped regions. Furthermore, another new operator allows you to transform 3D points using a rigid 3D transformation that is specified as a dual quaternion. And finally, HDevelop’s Matching Assistant now generates the code based on Generic Shape Matching.
Original image
Contrast improved using adaptive histogram equalization
NEWEST HALCON FEATURES
HALCON 21.11: Highly accurate identification and measurement of bacteria in petri dish
DEEP LEARNING INSTANCE SEGMENTATION
With HALCON 21.11 MVTec extends the functional scope of its deep learning features with a new technology called “instance segmentation”. This combines the advantages of semantic segmentation and object detection. With the help of instance segmentation, objects can be assigned to different classes with pixel accuracy. This technology is particularly useful in applications where objects are very close to each other, touch or overlap. Typical use cases also include grabbing randomly arranged objects from boxes (bin picking) as well as identifying and measuring naturally grown structures.
IMPROVED BAR CODE READER FOR CODE 128
With HALCON 21.11, HALCON’s bar code reader is improved in terms of robustness in case of blurred Code 128/GS1-128 codes. Now, codes with a larger amount of blur can be read. Blur on such codes can occur due to motion or due to limitations in depth of focus. The Code 128/GS1-128 is a widely used bar code type that is frequently used in logistics due to its compact size and high data density.
HALCON 21.11 reading a blurred Code 128 bar code
HALCON 21.11 Improved Dictionary Handling
IMPROVED DICTIONARY HANDLING
Dictionaries make it easy and convenient to manage complex data in HALCON. For example, different data types such as images, ROIs and parameter settings can be bundled in a single dictionary. This allows programs to be structured in a logical way, for example when passing many parameters to a procedure. HALCON 21.11 includes several improvements that make the handling of dictionaries even easier and faster. For example, dictionaries can now be initialized with a single operator call, and the syntax for adding and retrieving elements has been simplified. In addition, the auto-completion now also suggests the keys contained in the dictionary, which further speeds up and simplifies working with dictionaries.
FUTURE-PROOF INTERFACE FOR SHAPE MATCHING
With Generic Shape Matching, HALCON offers user-friendly access to MVTec's industry-proven shape matching technologies. Thanks to the significant reduction in the number of required operators, users can implement solutions more easily and quickly. With HALCON 21.11, existing functionalities are enhanced based on customer feedback to further increase usability. For example, the clutter feature has been integrated, handle inspection has been optimized, and additional parameters have been integrated and included in the automatic parameter estimation.
HALCON 21.11 Future-Proof Interface for Shape Matching
Scene with many objects or edges
IMPROVED SURFACE-BASED 3D-MATCHING
In HALCON 20.11, the core technology, edge-supported surface-based 3D-matching, is significantly faster for 3D scenes containing many objects and edges. In addition to this speedup, the usability has been improved by removing the need to set a viewpoint.
DOTCODE AND DATA MATRIX RECTANGULAR EXTENSION
In HALCON 20.11, the data code reader has been extended by the new code type, DotCode. This type of 2D code is based on a matrix of dots. It can be printed very quickly and is especially suitable for high speed manufacturing lines, like those used in the tobacco industry. Furthermore, the ECC 200 code reader now supports the Data Matrix Rectangular Extension (DMRE).
DotCode
Localized grouped characters with Deep OCR
Deep OCR
Deep OCR is a holistic deep-learning-based approach for OCR. This new technology brings machine vision one step closer to human reading.
Compared to existing algorithms, Deep OCR can localize characters much more robustly, regardless of their orientation, font type and polarity. The ability to automatically group characters allows the identification of whole words. This strongly increases the recognition performance since, e.g., misinterpretation of characters with similar appearances can be avoided.
IMPROVED SHAPE-BASED MATCHING
In HALCON 20.11, the core technology, shape-based matching, has been improved.
More parameters are now estimated automatically. This increases usability as well as the matching rate and robustness in low contrast and high noise situations.
Good results despite of low contrast
Screenshot of HDevelop
HDevelop Facelift
For enhanced usability, HALCON’s integrated development environment HDevelop has been given a facelift.
In HALCON 20.11, more options for individual configurations have been implemented, featuring e.g., a dark mode and a new modern window docking concept. Moreover, themes are now available to improve visual ergonomics and to suit individual preferences.
DEEP LEARNING EDGE EXTRACTION
Deep learning edge extraction is a new and unique method to robustly extract edges (e.g., object boundaries) that comes with two major use cases.
Especially for scenarios where a variety of edges is visible in an image, it can be trained with only few images to reliably extract the desired edges. Hence, the programming effort to extract specific kinds of edges is highly reduced with this version of MVTec HALCON. Besides, the pretrained network is innately able to robustly detect edges in low contrast and high noise situations. This makes it possible to extract edges that usual edge detection filters cannot detect.
MVTec's deep learning edge extraction can distinguish between
parquet joints and the wood grain.
AGRICULTURE & FOOD
Identification of natural products,automated fruit picking and sorting, or fill level measurement: HALCON is a technology for producers and packagers alike to achieve efficient and consistent production and keep up with the ever-changing demands of consumers.
AUTOMOTIVE & ROBOTICS
Determine the 3D pose of objects, extract 3D data for bin picking or robot path planning: HALCON's unique 3D vision techniques open new possibilities for numerous automotive and robotics applications.
LOGISTICS & PACKAGING
Quality control, completeness inspection, identification, or bar & data code reading: HALCON offers outstanding methods in all areas of logistics and packaging.
ELECTRONICS & SEMICONDUCTORS
Precise assembly, surface inspection or defect detection during the entire manufacturing process: With HALCON, system manufacturers are fully equipped to implement advanced processes at reduced costs.
3D GRIPPING POINT DETECTION
HALCON 22.11 combines 3D vision and deep learning for the first time. The 3D Gripping Point Detection can be used to robustly detect surfaces on any object that is suitable for gripping with suction. In contrast to classic bin-picking applications, the 3D Gripping Point Detection is a CAD-less approach, hence no prior knowledge of the respective objects is required. This increased flexibility opens up completely new application fields, such as those in the logistics industry or warehouses.
NEW DATA TYPE “MEMORY BLOCK”
As of HALCON 22.11, users can store and transfer binary data (e.g., images) in HALCON as well as further process it with other applications. This increases the software’s compatibility with machine communication protocols, such as OPC UA or image acquisition interfaces.
PROTECTION OF TRAINED DEEP LEARNING MODELS
For machine vision applications, the protection of intellectual property is getting more and more important. This is particularly relevant in the field of deep learning. The special aspect regarding this technology is that compared to traditional methods, the quality depends not only on the algorithm itself but also significantly on the quality of the training data. A large part of the effort of deep learning applications is in collecting the data and training the models. Therefore, HALCON 22.11 includes a new encryption mechanism for HALCON data types. One major use case is the encryption of deep learning models. This allows customers to protect their investment and know-how. Thanks to this, it is ensured that only authorized users can use and view their deep learning model.
BETTER TRACEABILITY OF DEEP LEARNING DECISIONS
A heatmap gives an indication of which areas of an image were decisive for the result of the deep learning model's classification. This can shed more light into the black box of deep learning, thereby increasing the traceability of corresponding processes. Guided Grad-CAM is a new method that now provides even more precise clues as to which regions of the image are relevant for the decision made by the deep learning network. For example, misclassifications can be investigated more precisely in a post-processing step.
NETWORK LICENSES
With HALCON 22.11, MVTec expands the licensing possibilities by adding the option to license HALCON via a network. A license server allows the use of floating licenses. Here, developers share a predefined number of licenses using a network connection. Customers benefit from cost savings due to multi-usage and greater flexibility in user allocation, developers enjoy greater independence and flexibility regarding their work location. Especially for distributed or remotely working development teams, this is the perfect way to effectively make use of HALCON’s powerful machine vision algorithms. Besides this, the new mechanism enables users to work in virtualized environments without permanent physical host ID.
DEEP OCR TRAINING
HALCON's Deep OCR enables users to efficiently solve text reading applications in a multitude of use cases. With HALCON 22.05, this technology is extended by training functionality, enabling application-specific training on the user's own application dataset. This allows you to solve even most complex applications like reading text with bad contrast (e.g., on tires). Another advantage is that very rarely used special characters or printing styles can also be trained. Training for Deep OCR significantly improves the performance and usability and makes applications run even more robust.
Reading text with bad contrast and lighting conditions on tires without ...
and with the new Deep OCR training.
GLOBAL CONTEXT ANOMALY DETECTION
HALCON 22.05 opens up completely new application possibilities with the detection of logical anomalies in images. This is the further development of the deep learning technology anomaly detection. Until now, it was possible to detect local, structural anomalies. The new “Global Context Anomaly Detection” is a one-of-a-kind technology, which is able to "understand" the logical content of the entire image. Just like HALCON`s existing anomaly detection the new “Global Context Anomaly Detection” only requires "good images" for training, eliminating the need of data labeling.This technology makes it possible to detect entirely new variants of anomalies like missing, deformed, or incorrectly arranged components. It opens up completely new possibilities: For example, the inspection of printed circuit boards in the semiconductor production or the inspection of imprints.
IMPROVED PRINT QUALITY INSPECTION FOR ECC 200 CODES
Print Quality Inspection (PQI) refers to the evaluation and grading of certain aspects of printed bar and data codes according to international standards. For example, it indicates how reliable a code can be read by various code readers or how stable the print quality is in a manufacturing process. HALCON supports various standards for grading the print quality of 1D and 2D codes. With HALCON 22.05, the PQI of data codes has been further improved. It is now up to 150% faster. In addition, the module grid determination for print quality inspection of ECC 200 has been improved. Last but not least, the usability of the PQI of data codes has been improved by introducing a new procedure that provides the grades.
QUALITY-OF-LIFE IMPROVEMENTS AND SPEED-UPS
With HALCON 22.05, various improvements are released. One example is a new operator that performs adaptive histogram equalization to improve contrast locally in an image. This helps to extract significantly more information from images with low contrast, especially in case of inhomogeneous gray value gradient. Besides, the HALCON library has been extended with a new operator which allows image smoothing with arbitrarily shaped regions. Furthermore, another new operator allows you to transform 3D points using a rigid 3D transformation that is specified as a dual quaternion. And finally, HDevelop’s Matching Assistant now generates the code based on Generic Shape Matching.
Original image
Contrast improved using adaptive histogram equalization
NEWEST HALCON FEATURES
HALCON 21.11: Highly accurate identification and measurement of bacteria in petri dish
DEEP LEARNING INSTANCE SEGMENTATION
With HALCON 21.11 MVTec extends the functional scope of its deep learning features with a new technology called “instance segmentation”. This combines the advantages of semantic segmentation and object detection. With the help of instance segmentation, objects can be assigned to different classes with pixel accuracy. This technology is particularly useful in applications where objects are very close to each other, touch or overlap. Typical use cases also include grabbing randomly arranged objects from boxes (bin picking) as well as identifying and measuring naturally grown structures.
IMPROVED BAR CODE READER FOR CODE 128
With HALCON 21.11, HALCON’s bar code reader is improved in terms of robustness in case of blurred Code 128/GS1-128 codes. Now, codes with a larger amount of blur can be read. Blur on such codes can occur due to motion or due to limitations in depth of focus. The Code 128/GS1-128 is a widely used bar code type that is frequently used in logistics due to its compact size and high data density.
HALCON 21.11 reading a blurred Code 128 bar code
HALCON 21.11 Improved Dictionary Handling
IMPROVED DICTIONARY HANDLING
Dictionaries make it easy and convenient to manage complex data in HALCON. For example, different data types such as images, ROIs and parameter settings can be bundled in a single dictionary. This allows programs to be structured in a logical way, for example when passing many parameters to a procedure. HALCON 21.11 includes several improvements that make the handling of dictionaries even easier and faster. For example, dictionaries can now be initialized with a single operator call, and the syntax for adding and retrieving elements has been simplified. In addition, the auto-completion now also suggests the keys contained in the dictionary, which further speeds up and simplifies working with dictionaries.
FUTURE-PROOF INTERFACE FOR SHAPE MATCHING
With Generic Shape Matching, HALCON offers user-friendly access to MVTec's industry-proven shape matching technologies. Thanks to the significant reduction in the number of required operators, users can implement solutions more easily and quickly. With HALCON 21.11, existing functionalities are enhanced based on customer feedback to further increase usability. For example, the clutter feature has been integrated, handle inspection has been optimized, and additional parameters have been integrated and included in the automatic parameter estimation.
HALCON 21.11 Future-Proof Interface for Shape Matching
Scene with many objects or edges
IMPROVED SURFACE-BASED 3D-MATCHING
In HALCON 20.11, the core technology, edge-supported surface-based 3D-matching, is significantly faster for 3D scenes containing many objects and edges. In addition to this speedup, the usability has been improved by removing the need to set a viewpoint.
DOTCODE AND DATA MATRIX RECTANGULAR EXTENSION
In HALCON 20.11, the data code reader has been extended by the new code type, DotCode. This type of 2D code is based on a matrix of dots. It can be printed very quickly and is especially suitable for high speed manufacturing lines, like those used in the tobacco industry. Furthermore, the ECC 200 code reader now supports the Data Matrix Rectangular Extension (DMRE).
DotCode
Localized grouped characters with Deep OCR
Deep OCR
Deep OCR is a holistic deep-learning-based approach for OCR. This new technology brings machine vision one step closer to human reading.
Compared to existing algorithms, Deep OCR can localize characters much more robustly, regardless of their orientation, font type and polarity. The ability to automatically group characters allows the identification of whole words. This strongly increases the recognition performance since, e.g., misinterpretation of characters with similar appearances can be avoided.
IMPROVED SHAPE-BASED MATCHING
In HALCON 20.11, the core technology, shape-based matching, has been improved.
More parameters are now estimated automatically. This increases usability as well as the matching rate and robustness in low contrast and high noise situations.
Good results despite of low contrast
Screenshot of HDevelop
HDevelop Facelift
For enhanced usability, HALCON’s integrated development environment HDevelop has been given a facelift.
In HALCON 20.11, more options for individual configurations have been implemented, featuring e.g., a dark mode and a new modern window docking concept. Moreover, themes are now available to improve visual ergonomics and to suit individual preferences.
DEEP LEARNING EDGE EXTRACTION
Deep learning edge extraction is a new and unique method to robustly extract edges (e.g., object boundaries) that comes with two major use cases.
Especially for scenarios where a variety of edges is visible in an image, it can be trained with only few images to reliably extract the desired edges. Hence, the programming effort to extract specific kinds of edges is highly reduced with this version of MVTec HALCON. Besides, the pretrained network is innately able to robustly detect edges in low contrast and high noise situations. This makes it possible to extract edges that usual edge detection filters cannot detect.
MVTec's deep learning edge extraction can distinguish between
parquet joints and the wood grain.
AGRICULTURE & FOOD
Identification of natural products,automated fruit picking and sorting, or fill level measurement: HALCON is a technology for producers and packagers alike to achieve efficient and consistent production and keep up with the ever-changing demands of consumers.
AUTOMOTIVE & ROBOTICS
Determine the 3D pose of objects, extract 3D data for bin picking or robot path planning: HALCON's unique 3D vision techniques open new possibilities for numerous automotive and robotics applications.
LOGISTICS & PACKAGING
Quality control, completeness inspection, identification, or bar & data code reading: HALCON offers outstanding methods in all areas of logistics and packaging.
ELECTRONICS & SEMICONDUCTORS
Precise assembly, surface inspection or defect detection during the entire manufacturing process: With HALCON, system manufacturers are fully equipped to implement advanced processes at reduced costs.
HALCON Editions
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