- Improved Surface-based 3D-Matching
HALCON 20.11 offers an
edge-supported surface-based 3D-matching which is significantly
faster for 3D scenes containing multiple objects and edges.
Additionally, the usability has been improved by removing the
need to set a viewpoint.
- DotCode and Data Matrix Rectangular Extension
code reader in HALCON 20.11 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.
the ECC 200 code reader now supports the Data Matrix Rectangular
- Deep OCR
Deep OCR is an 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 for example,
misinterpretation of characters with similar appearances can
Increased Shape-based Matching
In HALCON 20.11, the shape-based matching
tool has been improved. This increases usability as well as
the matching rate. It also increases robustness in low contrast
and high noise situations.
In HALCON 20.11, more options for individual viewing configurations
have been implemented. The changes feature a dark mode with white
text on a black background. A new modern window docking concept
allows windows to be repositioned. Moreover themes are now improved
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. There are two major use cases for this new tool.
The first case is where images have a wide variety of edge types
visible. The tool can be trained with only a few images to reliably
extract all desired edges. Hence the programming effort to extract
specific types of edges is highly reduced. The second major use
case, is where edges are low contrast and in high noise situations.
The tool is innately able to robustly detect these edges. It makes
it possible to extract edges that traditional edge detection filters
HALCON 20.11 introduces a new HALCON/Python interface. This enables
developers who work with Python to easily access HALCON's powerful
Dubbed "the power behind machine vision",
HALCON is a fast, comprehensive and powerful software for all demanding
areas of machine vision applications such as position recognition,
object identification, fault-detection, code reading, print quality
inspection, surface inspection, remote sensing and aerial imaging,
and all complex 3D-vision tasks.
With each new version, you can apply cutting-edge,
deep-learning algorithms to your application with HALCON's ability
to train Convolutional Neural Networks. After training, the network
can be used to classify new data. Typical application areas for
this deep learning technology is in the field of defect classification
(circuit boards, bottle mouths, pills... and more) or object classification
(e.g. identifying the species of a plant from one single image).
Handcrafting of features is no longer necessary with Deep Learning.
HALCON's powerful library includes a wide variety
of operators and also provides interfaces to hundreds of cameras
and frame grabbers. Additionally, HALCON secures your investment
by supporting most common standards, operating systems and programming
Quick and efficient building of imaging solutions
is possible with HALCON'S highly-interactive and integrated development
environment saving costs and improving time-to-market.