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  • Edge Computing

    Using cloud-based image processing can increase latency and network traffic. It can also pose privacy and security risks.

  • Embedded Systems for Machine Vision

    Embedded systems are computers designed for integration with larger pieces of equipment. The computers built into cars, medical instrumentation, and consumer devices like smart TVs are all examples of embedded systems.

  • Exmor R / STARVIS

    The Sony Exmor R and STARVIS families of rolling shutter, global reset sensors provide excellent low-light imaging for visible and NIR light. For applications including microscopy, metrology, and laparoscopy, where a global shutter sensor is not required, the small pixels of Exmor R sensors enable high-resolution sensors in smaller optical formats.

  • HNu Photonics Studies the Effects of Spaceflight on Human Physiology using Blackfly Machine Vision Cameras

    HNu Photonics is a developer of cutting-edge flight and ground based hardware for aerospace and defense sectors. Using Teledyne FLIR machine vision cameras, the SCORPIO-V division of HNu Photonics is working with NASA to better understand how spaceflight and microgravity impacts humans on a cellular level.

  • Comparing Deep Learning Cameras with Smart Cameras

    The Firefly® DL camera provides an easy path to deploying trained networks in the field.

  • Instrumental’s Novel AI Inspection Platform Leverages Teledyne FLIR Machine Vision Cameras

    Manufacturing accounts for more than half of the world’s total gross world product (GWP) at $40 trillion, but 20% of every dollar spent goes to waste, according to manufacturing optimization company Instrumental. This represents a problem worth $8 trillion, or 10% of the GWP. Founded in 2015 by former Apple engineers, Instrumental has developed an optimization and inspection platform combining cloud software, machine vision inspection, artificial intelligence (AI), and electronic test data that aims to reduce waste by enabling engineers to not only stop problems at the end of the inspection line but to fix them upstream as well. 

  • Interfaces for Machine Vision

    Choosing the right interface for your machine vision application is a key decision in your camera selection process. The following sections provide an overview of the different types of cables and connectors available for machine vision applications along with associated pros and cons.

  • Understanding USB 3.1 and USB 3.2

    The USB Implementers Forum has updated USB 3.0 to USB 3.1. FLIR has updated its product descriptions to reflect this change. This page explains USB 3.1, as well as the differences between USB 3.1 Generation 1 and Generation 2 and the practical gains each offers machine vision developers. The USB Implementers Forum has also published specifications for the USB 3.2 standard that doubles USB 3.1 throughput.

  • Vision-Guided Robot Trims Tomato Plants with Chameleon3

    A robot that provides tomato growers with an economically viable alternative to manually de-leafing tomato crops grown in greenhouses.

  • INTELLIGENT COUNTING: BEYOND ACCURACY

    See how FLIR sensors deliver deeper and more meaningful accuracy for your counting and tracking needs

  • Introduction To Spinning Disk Confocal Microscopy

    There are two significant challenges in biological imaging that conventional fluorescence microscopy cannot overcome. Firstly, biological specimens are 3-dimensional structures so to fully understand them we often need to construct 3-dimensional images.

  • Expansion Microscopy

    Light microscopy techniques have been vital to our understanding of biological structures in cells and tissues since their invention in the late 16th century. However, the resolution of conventional light microscopy techniques is limited by the diffraction limit of light which prevents structures smaller than approximately ~300 nm from being resolved.