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Vision CampusCamera Technology Interfaces and Standards Markets and Applications Vision Systems and ComponentsCamera TechnologyComparison between Sony's IMX CMOS sensor series Why should I color calibrate my camera? Expert Tips to Find the Right Lens for a Vision System Expert Tips for Embedded Vision Systems What Is Multispectral Imaging? Comparison of CMOS cameras Color in Image Processing Processing Boards in Embedded Vision What Is Image Processing 3D Technologies in Image Processing What Is Embedded Vision Why CMOS Image Sensors? What Is Time of Flight? What Is Image Quality? Camera Sizes How does a Digital Camera Work? CMOS vs. CCD: Sensor Technology Real-Time Capability NIR: Seeing Clearly Even in Low Light High-Sensitivity Industrial CamerasShow moreShow lessInterfaces and StandardsSystem Setup with CoaXPress 2.0 What Is CoaXPress? Which interface for Embedded Vision? Multi-Camera Systems with GigE 2.0 USB 3.0 – Interface of the Future What Is an Interface? Camera Link Gigabit Ethernet (GigE) GenICam Standard USB 3.0 and USB3 VisionMarkets and ApplicationsCameras for Fluorescence Microscopy What Is the Role of Computer Vision for Industry 4.0? What Is Deep Learning? Robots with Vision Technology Blockchain for IoT Sensor Producers How will IoT change retail? How Do Machines Learn? IoT Applications in the Smart City Benefits of Cameras in Medicine Lab Automation with Vision Medicine with Vision Image Processing in Industry 4.0 Machine Vision Automated Optical Inspection Color Calibration in Medical TechnologyShow moreShow lessVision Systems and ComponentsSoftware in Image Processing Image Pre-processing Strengthens and Streamlines Image Processing Systems How to Find the Right Lighting for Your Vision System? What is a Machine Vision SDK Lighting How Can I Find the Right Lens? Components of a Vision System Cutting Through the Noise: Camera Selection Image Processing Systems — The Basics
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Vision Systems for Deep Learning
Deep learning is rapidly spreading across computer vision applications. The benefits of artificial neural networks (ANNs) are twofold. ANNs have the potential to improve the accuracy and robustness for applications in factory automation, robotics or retail. At the same time, ANNs have the capability to solve image-based application problems that could not be solved in the past, such as pathology detection in microscopy or complex pattern classification in surface detection.
Comparison of classical image processing with deep learning
Are neural networks the future of machine vision?
How do apples differ from pears? Up to now, developers have set differentiation criteria. Neural networks learn these criteria on their own. Learn what it takes for them to accurately apply what they’ve learned.
Artificial intelligence in image processing
Thanks to deep learning-based artificial intelligence (AI), there are numerous opportunities in industrial image processing. Learn about the advantages and disadvantages compared to conventional methods.
Camera systems for deep learning applications
Deep learning algorithms enable an increasing amount of new applications that were previously considered unsolvable for machines. Find out about vision systems for deep learning models in Medical & Life Sciences.
Neural networks conquer image processing
Deep learning increasingly takes over tasks handled by conventional algorithm-based image processing, as this approach yields better image processing results in many applications. For some applications, deep neural networks such as convolutional neural networks (CNN) are particularly well suited.
Neural networks on FPGAs in the industrial sector
Neural networks such as convolutional neural networks (CNN) on FPGAs take over classical image processing tasks in industry. If these are exclusively, more easily or better solvable with deep learning, it displaces classical image processing – especially in the case of disturbances such as reflective surfaces.
Deep vision becomes reality with CNN
Small neural networks are sufficient for many vision applications. Processors such as FPGAs can therefore be used for convolutional neural networks (CNN). This opens up a wide range of applications far beyond classification tasks and also enables use in embedded vision systems.
Automating thought – opportunities & risks
Billions are being invested worldwide in the use of artificial intelligence (AI) in a wide range of social areas. At the same time, laws, standards and ethical guidelines are being enacted to ensure sensible use of this technology – and also to emphasize its benefits to society.
Smart payment terminal with AI software and Basler cameras for retail
Basler has developed an automated retail payment terminal made of embedded technology and artificial intelligence (AI) software. It identifies and classifies products in a shopping cart and displays the prices.
AI-based bacteria classification with an embedded vision system
Embedded technologies are finding their way into diagnostics and analytics. They are powerful, space-saving and inexpensive. Add an artificial neural network (ANN) and bacteria can be classified in a split second.
Watch Use Case video
3D vision combined with deep learning software for automated fruit recognition
Basler and Data Spree have developed an accurate and robust real-time solution for sorting fruit. The vision system is based on a Basler blaze time-of-flight camera and a deep learning platform from Data Spree.
Deep learning in action: vision sensor “at the edge” with Amazon Web Services
Do you want to develop an embedded vision sensor to detect and classify objects? Basler has set up a convolutional neural network (CNN) with a deep learning framework and is using this in an embedded device.
Camera module provides image data in smart camera solution
With the SmartCam, the Wahtari company offers a camera solution with onboard image processing and integrated accelerator for artificial intelligence (AI). Part of the smart solution is the powerful Basler dart camera module, which currently offers the most compact design at the selected resolution and frame rate.
What is deep learning?
Terms such as machine learning, deep learning or artificial intelligence (AI) are being used in more and more areas of everyday life. What exactly does this mean, how does it all work and what are its application areas?
How do machines learn?
How do you teach a machine to learn autonomously? How does a machine become an “intelligent” machine with the help of neural networks and what can we use this called artificial intelligence (AI) for?
Watch Vision Campus Video