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  • Kinetix Pre-Arrival Guide

    Thank you for your interest in the Kinetix sCMOS, the next generation in scientific CMOS cameras. This is a guide to help you prepare for the arrival of the Kinetix, ensuring that the hardware and software of both your PC and microscope/imaging system are capable of making the most of the power of the Kinetix and delivering your desired data.

  • Prime 95B Pre-Arrival Guide

    Thank you for your interest in the Prime 95B sCMOS, the world’s first back-illuminated sCMOS that delivers the highest sensitivity for the most demanding applications. This is a guide to help you prepare for the arrival of the Prime 95B, ensuring that the hardware and software of both your PC and microscope/imaging system are capable of making the most of the power of the Prime 95B and delivering your desired data.

  • Prime BSI Express Pre-Arrival Guide

    Thank you for your interest in the Prime BSI Express sCMOS, a highly optimized imaging solution. This is a guide to help you prepare for the arrival of the Prime BSI Express, ensuring that the hardware and software of both your PC and microscope/imaging system are capable of making the most of the power of the Prime BSI Express and delivering your desired data.

  • Prime BSI Pre-Arrival Guide

    Thank you for your interest in the Prime BSI sCMOS, which delivers the perfect balance between high-resolution imaging and sensitivity. This is a guide to help you prepare for the arrival of the Prime BSI, ensuring that the hardware and software of both your PC and microscope/imaging system are capable of making the most of the power of the Prime BSI and delivering your desired data.

  • TECHNOLOGY INNOVATION

    FLIR’s constant innovation in sensor hardware and software results in industry leading accuracy.

  • Best Practices: Training a Deep Learning Neural Network

    If developers need to run deep learning inference on a system with highly limited resources, they can optimize the trained neural network accordingly and eliminate the need for a host system. Much smaller devices like the upcoming FLIR® Firefly® camera can run inference based on your deployed neural network on its integrated Movidius™ Myriad™ 2 processing unit. This article describes how to develop a dataset for classifying and sorting images into categories, which is the best starting point for users new to deep learning.

  • Comparing VPUs, GPUs, and FPGAs for Deep Learning Inference

    A key decision when getting started with deep learning for machine vision is what type of hardware will be used to perform inference. Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Vision Processing Units (VPUs) each have advantages and limitations which can influence your system design.

  • Complete off the shelf 3D system

    High Definition Imaging (HDI) 3D Scanner which produces a digital 3D scan from physical objects in less than two seconds.

  • SIM and iSIM

    One of the goals of biological microscopy is to observe and analyze biological processes and structures on the subcellular scale. However, the size of the smallest structures that can be observed is set by the diffraction limit of light, meaning no detail can be resolved smaller than around 250 nm.

  • 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.