Webinar: machine vision in intralogistics and automated warehouses

In this webinar, Jan Gaertner (Product Manager HALCON) and Agnes Weinhuber (Application Engineer) guide you through the new features of the new version of HALCON.

Learn how to turn a promising AI inspection proof of concept into a production-ready solution. This webinar shows how to manage image data, develop the complete application, and integrate it into a real production environment with MVTec MERLIC.

Learn all about MVTec MERLIC 26.03 in this webinar presented by Ulf Schulmeyer (MERLIC Product Manager) and Florian Aumeier (Application Engineer).

In this webinar, Jan Gärtner (Product Manager HALCON) and Neelkant Kashyap (Application Engineer) guide you through the new features of the new version of HALCON.

Learn how to bring deep learning into real machine-vision applications with a clear, practical roadmap. Since 2017, MVTec has delivered production-ready AI that runs reliably on the factory floor.

Learn all about MVTec MERLIC 5.8 in this webinar presented by Ulf Schulmeyer (MERLIC Product Manager) and David Hadlich (Application Engineer).

Explore HALCON 18.11: new deep learning for classification, segmentation, detection, plus developer-friendly dictionaries.

Excerpt from our 23.11 HALCON webinar, where the new features of the Software Manager were presented, including the new registration feature.

Deep 3D Matching: Fast, robust 3D object detection from 2D images—perfect for bin picking and robotics, even in challenging conditions.

Inspecting wafers is extremely challenging due to their reflective surfaces, microscale defects, and the need for high precision. This is where machine vision makes the difference: by enabling automated, robust, and highly efficient optical inspectio…

In this webinar, Jan Gärtner (Product Manager HALCON) and Neelkant Kashyap (Application Engineer) guide you through the new features of the new version of HALCON.

Learn all about MVTec MERLIC 5.7 in this webinar presented by Ulf Schulmeyer (MERLIC Product Manager) and Xin Jin (Application Engineer).

In this webinar, Jan Gärtner (Product Manager HALCON) and Benedikt Wörz (Application Engineer) guide you through the new features of the HALCON 24.11.

Learn all about MVTec MERLIC 5.6 in this webinar presented by Ulf Schulmeyer (MERLIC Product Manager) and Xin Jin (Application Engineer).

What does an automated quality inspection workflow with machine vision look like? Find out how deep learning can improve the efficiency of your production line.

In this tutorial, we show how we set up image acquisition with line scan cameras in MVTec HALCON—from trigger setup to shading correction for accurate results.

In this video, we would like to give you a first introduction to line scan cameras.

HALCON, MERLIC, and the DLT just one download away. In this tutorial, you will learn about the MVTec Software Manager.

In this tutorial, you will learn how to use MVTec MERLIC’s Image Source Manager (ISM).

In this tutorial, you will learn about XYZ mappings in MVTec HALCON, why they are useful, and how to work with them efficiently.

In this tutorial, we’ll have a look at 3D object models within MVTec HALCON.

In this tutorial, you will learn about using deep-learning-based global context anomaly detection in MVTec HALCON.

In this tutorial, we show how we use MVTec HALCON and deep learning to let a robot check supermarket shelves after hours and classify each slot as either full or empty.

Another successful implementation of HALCON’s deep learning technologies in the classification of leather types and quality inspection with two real-world applications.

MVTec’s HALCON developed a combination of rule-based and deep-learning-based approaches for faster, easier and more precise defect detection in the metalworking industry.

MVTec’s research department has taken anomaly detection to the next level with a comprehensive dataset and created 30 different anomaly detection scenarios

After watching this tutorial, you will know how to use the MVTec Deep Learning Tool to train and evaluate a model for Global Context Anomaly Detection.

In this tutorial, you will learn how to use MVTec MERLIC’s “Detect Anomalies in the Global Context”-Tool, which features MVTec’s state of the art technology Global Context Anomaly Detection. This tool allows MERLIC users to detect logical and structu…

Using MVTec’s HALCON deep learning, the quality control system for baked goods is able to analyze up to 140 pieces per minute 24 hours a day!

Using 2D Deep Learning technologies, MVTec’s HALCON is able to identify where the goods will be stored and if there is enough storage space on shelves.

To inspect the welding process of its modular BLS 500 laser system and fully automate it, Manz, a high-tech machine manufacturer, relies on machine vision using the standard software MVTec HALCON.

Deep learning evaluation reports just got easier to generate with MVTec’s Deep Learning Tool! Watch the video to see how.

In this tutorial, we explain when camera calibration is needed, how it works in MVTec HALCON, and what to consider to achieve reliable measurement results.

In this tutorial, you will learn about deep-learning-based global context anomaly detection by MVTec. With this technology, you can detect structural and logical anomalies in your images.

In collaboration with MVTec's distributor, The Imaging Source, industrial automation solutions provider, Teomsi Electric Company developed an inspection system in cooperation with Ecolor for their furniture factory.

For a customer in the field of veterinary medicine, Pose Automation GmbH, a Certified Integration Partner of MVTec, developed a tailor-made robotic cell in which catheters are sorted into a packaging machine. The robust and flexible machine vision so…

Deep OCR is a holistic deep-learning-based approach for OCR. This new technology brings machine vision one step closer to human reading.

In this video-tutorial, we will take a deep dive in 3D transformations and poses with MVTec HALCON.

Automated defect inspection of train rooftops: With MVTec HALCON, high-speed train inspections shrink from 1.5 hours to just 10 minutes—all while trains are moving!

Going on vacation but need to supervise the machines? See how Tom uses HALCON generic sockets to relax and enjoy his vacation without worrying about work.

The implementation of robotics and sensing technologies as an alternative solution makes crop production more efficient and sustainable.

Japanese company Kett Electric Laboratory develops and sells an automated inspection system for rice grains, achieving human-level or even superior quality control with MVTec HALCON.

The Swiss Safety Center AG, part of a center of excellence in technical safety and risk management, offers a full portfolio for industry, retail, and commerce.

In this video, we show how MVTec HALCON and MERLIC solve even unusual tasks—from finding safe landing spots to reading tiny text in seconds.

When using shape-based matching, there are many parameters and settings you might want to tweak if you have images where the standard parameters don’t find all matches. Find out how!

Learn about the main feature of Deep Learning Tool 22.06: Efficiently labeling datasets to retrain HALCON’s Deep OCR and boost recognition rates.

Incorrectly packed toast packages lead to disruptions in the production process. Find out how this challenge was solved with MERLIC 5.

You want to count the number of people that are entering and leaving a building? Watch this MVBite and learn all about it.

Join our research engineer Patrick to see how easy and robust the labeling works even for non-industrial objects.

In this tutorial, we show how to work with XLD contours in MVTec HALCON—detecting lines and circle segments, measuring widths, and analyzing geometric shapes.

Have a look at deep-learning-based instance segmentation with MVTec HALCON and locate objects in an image.

In this tutorial, you will learn how to label instance segmentation data with the MVTec Deep Learning Tool.

In this tutorial, we show how to create and use dictionaries in MVTec HALCON: store and access values, work with JSON, and pass them to and from procedures.

In this tutorial, we show how we use MVTec MERLIC’s “Read Text and Numbers with Deep Learning” tool to apply Deep OCR, visualize results, and improve processing time.

Watch the generic shape matching workflow tutorial and learn how to set up and run shape matching in MVTec Vision Solutions.

In this tutorial, we show how we work with control tuples in MVTec HALCON—creating, selecting, comparing, converting them and using them efficiently in HDevelop.

In this tutorial, we show how to perform 3D reconstruction with a stereo setup in MVTec HALCON—from disparities to surface fusion for robust point clouds.

In this tutorial, you will learn how to use MVTec MERLIC’s new “Classify Image”-Tool together with the Deep Learning Tool.

In this tutorial, we show how to use MVTec MERLIC’s deep-learning “Detect Anomalies” tool to inspect glass bottles for local defects using only a few good samples.

In this tutorial, we show how we use HDevelop and MVTec HALCON to set up a quick hand-eye calibration between a 3D sensor and a robot for reliable pick-and-place.

We show how farming machinery uses MVTec machine vision and multispectral image acquisition to detect weeds in real time and spray herbicide only where needed.

With Deep OCR, MVTec introduces a holistic deep-learning-based approach for optical character recognition (OCR).

The automation of processes in greenhouses using machine vision technologies makes it possible to better meet the increasing demand and requirements for quality, hygiene, and food safety.

In this video, we show how MVTec MERLIC uses deep-learning-based anomaly detection to inspect PCBs for defects – with only good samples and no programming.

In this tutorial, you'll learn how to apply print quality inspection to bar and data codes with MVTec HALCON.

With the MVTec Deep Learning Tool, it’s possible to train a deep-learning-based classification model from scratch.

MVTec MERLIC is an all-in-one software product for quickly building machine vision applications without programming.

In this tutorial, you’ll learn about some of the most commonly used image filters in MVTec HALCON, how they work, and what they can be used for.

In this tutorial, you will learn how to train a deep-learning-based Anomaly Detection model for your own application.

In this tutorial, we show how to read bar codes with MVTec HALCON—from using the basic operators to tuning, debugging, and exploring HDevelop examples for challenging codes.

In the first part of this tutorial series on HALCON's object detection, you will learn what object detection actually is, and what kinds of applications it can be used for.

In the second part of this tutorial series on HALCON’s object detection, you will learn how to train a deep-learning-based object detection model with MVTec HALCON.

In the third part of this tutorial series on HALCON’s object detection, we will evaluate the deep-learning-based object detection model we trained in the previous video.

In this tutorial, we use a trained deep-learning object detection model in MVTec HALCON for inference and show how to process and validate the detected objects.

In this tutorial, we’ll take a look at the recommended workflow when working with deep learning in MVTec HALCON.

HALCON's deep-learning-based anomaly detection allows you to easily detect whether images contain deviations from known training data.

Learn how to seamlessly turn your HDevelop procedures into native C/C++ functions. This tutorial walks you through exporting, compiling, and integrating HALCON code directly into your own application.

Learn how to draw oriented rectangles with the MVTec Deep Learning Tool in this step-by-step video tutorial.

From setup to 3D output: learn how to calibrate multiple cameras in HDevelop and verify precision.

In this tutorial, we show how to use HDevEngine to integrate HDevelop machine vision code into an existing Visual Studio project – without recompiling your application.

Explore surface-based matching with HALCON. This tutorial shows how to detect and align textured 3D objects using advanced pattern matching techniques.

Improve HALCON’s 3D surface matching by cleaning input data and using 2D xyz-mappings for faster, more robust results.

Handle small batches fast: MERLIC recipes let the PLC reconfigure or switch vision apps on demand.

Get started with surface-based matching in HALCON. This introductory tutorial explains key concepts and basic steps for detecting textured 3D objects.

This webinar gives users of machine vision software a better understanding of deep learning technologies and the benefits for machine vision applications.

Mix 3D height data and classic 2D vision in MERLIC to verify both compartments quickly and reliably.

Train custom CNNs in HALCON for classification, detection, and segmentation, then deploy to analyze new images.

Learn hand-eye calibration in HALCON: align robot and camera coordinates for precise pick-and-place.

Discover pick-and-place with shape-based matching in HALCON. This tutorial demonstrates how to locate parts reliably and guide robotic pick-and-place tasks.

Learn how to debug an HDevEngine application on an embedded device. This tutorial shows tools and techniques to find and fix issues in HALCON automation projects.

Built during MVTec’s creative “woikn-days,” this fun example turns machine vision into a virtual game.

Learn how to simply acquire and use live images within HDevelop: using the Image Acquisition Assistant, we simply generate the required code. Additionally, with the respective tools available in HDevelop, we focus the image and adapt the aperture. La…

Bring HALCON’s full power to embedded devices—standard ARM/Linux support and proven ports across platforms.

Discover HALCON on Arm: evaluate your embedded device, explore HPeek, and start building your own vision solution.

Use HALCON Variable Inspect in Visual Studio to view images, regions, and variables from your HALCON code instantly.

In this tutorial, we show how we use MVTec HALCON to segment images with the Gray Histogram, split touching objects using morphology, and cluster regions by their features.

Read use-by dates with MERLIC’s deep-learning text tool on standard IPC, camera, and PLC—end-to-end and ready for industry.

In this tutorial, we show how to use MVTec HALCON with a stereo vision system to acquire 3D point clouds, segment objects, and reliably detect a cylinder.

In this video, we show how MVTec MERLIC uses Deep OCR with pre-trained features and large labeled datasets to reliably read new text and number data.

See how MERLIC inspects bottles reliably. This tutorial shows setup, lighting, and vision logic for automated bottle quality checks.

Learn calibrated 1D measuring with HALCON. This tutorial covers setup and practical steps for accurate length measurement in machine vision.

Learn 2D metrology in HALCON to measure circles, ellipses, rectangles, and lines with precision.

Need precise measurements or distortion-free images? Learn how to calibrate your camera in MERLIC using a simple calibration plate.

In this tutorial, we show how we use MVTec MERLIC’s alignment tools to automatically align moving parts, enabling robust positioning and precise measurements.

HALCON is the comprehensive standard software for machine vision with an integrated development environment (IDE) that is used worldwide.

Build in MERLIC Backend, design in MERLIC Designer: create interactive frontends and tune parameters live for scratch detection on metal.

Discover how shape-based matching works in MVTec HALCON. This introductory tutorial explains core concepts and shows how to detect objects reliably—even with rotation, scale changes, or partial occlusion.

In this tutorial, we show how to process matching results in MVTec HALCON—count matches and realign regions or images based on the detected positions.

In this tutorial, we show how to optimize shape-based matching in MVTec HALCON using advanced parameters—making it faster and more robust, even with blur or deformation.

HALCON 12.0.2 decodes what others can’t — from thickened bars to faded codes. This video demonstrates the power of HALCON’s barcode reader.

With easyTouch in MVTec MERLIC, we show how we simply hover to detect, align, measure, or read text – making machine vision setup fast and intuitive.

Real-time shrimp sorting with HALCON boosts yield and returns live by-catch to the sea—fast, accurate, sustainable.

This MVTec HDevelop tutorial improves visualization: apply LUTs, fit window to image, set fonts/colors, show outlines, and use the correct coordinate system.

In this tutorial, we show how we use MVTec MERLIC to detect missing fuses on a PCB in just a few minutes – easy to follow along with the free trial version.

Develop in HDevelop, run on D3: set ROI, learn shapes, measure diameters, save scripts. Registered users can download HALCON.

MVTec’s HDevelop tutorial: read and use iconic vs. control variables. Use tuples and arrays to avoid loops. Includes a sample program download.

Learn HDevelop essentials: Graphics, Program, Operator, Variable windows, plus step-through debugging with counter and breakpoints.

MVTec MERLIC Designer: Create your own frontend for machine vision applications in just a few steps—interactive, customizable, and easy to use.

Learn how to use iconic and control variables in HDevelop, plus smart use of tuples and arrays to avoid loops in your program.

In this video, we show how to use the HDevelop Example Browser in MVTec HALCON to quickly find example programs by application area, industry, or keyword.

All-in-one and code-free: MERLIC pairs MVTec expertise with easyTouch for speed, reliability, and cost savings.

In this video, we show how MVTec HALCON enables machine vision solutions—from live image acquisition and examples to exporting your application.

This video shows how the collaborative robot NEXTAGE uses MVTec HALCON for precise image processing, enabling flexible automation in modern production.

This demo by LinX shows how fast and robust MVTec HALCON segments and analyzes candies and seeds using its powerful blob analysis.

This demo shows how Sensor Control uses MVTec HALCON to perform 3D camera calibration and 3D model creation for robot guidance in under one minute.

HALCON’s Sample-based Identification (SBI) recognizes thousands of objects by color or texture: fast, robust, and easy to use, even without bar codes or data codes.

With HALCON 3D vision, robots pick disordered automotive parts precisely using sheet-of-light scanning and smart tool selection.

HALCON Embedded on the D3 smart camera: Detect, measure, and analyze in real time – powerful machine vision right on the device.

Affordable 3D imaging made simple: HALCON and Leuze’s LPS36 enable precise, real-time vision for pick-and-place robots.

Robust bin picking of heavy parts with sheet-of-light and HALCON 3D; 2D re-localization and dual-part pickup feed welding.