Newest HALCON Features
On this page you will find information on the newest features of MVTec's standard machine vision software HALCON. You will find out more about what's included in the latest version available, as well as get a sneak preview on what will be included in the next version.
The new HALCON version will be released this December and – reflecting the release date – it will be named: HALCON 17.12. Below, you will find a first preview of what will be included in this coming version.
HALCON 17.12 will be the first release of the new HALCON Progress Edition. To learn more about the different HALCON editions, please click here.
Deep Learning out of the Box
With HALCON 17.12, users will be able to train their own classifier using CNNs (Convolutional Neural Networks). After training the CNN, it can also be used for classifying new data with HALCON.
Training a CNN
Training a CNN in HALCON is done simply by providing a sufficient amount of labeled training images. E.g., to be able to differentiate between samples that show scratches or contamination and good samples, training images for all three classes must be provided: Images showing scratches must be labeled "scratch", images showing some sort of contamination must carry the label "contamination", and images showing a good sample must be in the category "OK".
HALCON then analyzes these images and automatically learns which features can be used to identify defective and good samples. This is a big advantage compared to all previous classification methods, where these features had to be "handcrafted" by the user – a complex and cumbersome undertaking that requires skilled engineers with programming and vision knowledge.
Using the Trained Network
Once the network has learned to differentiate between the given classes, e.g., tell if an image shows either a scratched, a contaminated or a good sample, the network can be put to work. This means, users can then apply the newly created CNN classifier to new image data which the classifier then matches to the classes it has learned during training.
Typical application areas for deep learning include defect classification (e.g., for circuit boards, bottle mouths or pills), or object classification (for example, identifying the species of a plant from one single image).
Inspecting Specular Surfaces with Deflectometry
Inspecting specular reflecting surfaces imposes special challenges, because the observer does not see the surface itself, but the mirror image of the environment. This poses significant problems for most surface inspection methods such as triangulation or shape from shading, because these usually rely on diffuse reflection.
HALCON 17.12 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 aforementioned specular reflections by observing mirror images of known patterns and their deformations on the surface.
The Latest Version – HALCON 13
To find out more about the numerous features and improvements of our latest version HALCON 13, please click here.