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2017-10-30

HALCON 17.12

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.
 

 

HALCON 17.12

 

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.

 


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).

 

The Latest Version – HALCON 13


To find out more about the numerous features and improvements of our latest version HALCON 13,

please click here.