Speed up your testing with real-time focusing in Imatest Master 2020.2.
Recent speed improvements allow for real-time focusing and allow users to analyze images from two types of sources:
- Direct image acquisition from a variety of devices, listed in detail in Supported image acquisition hardware for Imatest Master.
- Image files listed in Image file formats and acquisition devices,
Although the majority of images traditionally analyzed by Imatest have been from files (JPG, PNG, etc.), three modules, which can perform a majority of Imatest’s analyses, support direct data acquisition, and can be used for realtime analysis.
Imatest Version 2020.2 adds several new features to Imatest Master and Imatest IT, including Improvements in Calculation Speed, Uniformity Statistics based on EMVA 1288 Standards, Uniformity measurements from ISO 17957:2015, IT For Mac OS, and Tunable Checkerboard Detection. (more…)
Do you have questions about our upcoming 2020.2 release features, our test lab equipment, or image quality testing? Join our live, online Q&A session on Wednesday, October 28th, from 10:00 am to 12:00 pm MST. Henry Koren, Director of Engineering, will lead the discussion. To participate, please register and submit questions in advance.
Webcams are an increasingly vital tool for working remotely and staying connected with friends and loved ones. As such, webcam sales have experienced significant growth. We all know good and bad quality images when we see them, but quantifying a camera’s performance is critical for making design choices, sourcing components, and performing quality control. Developing these devices presents challenges common to the development of consumer cameras. (more…)
We discuss several common image quality measurements that are often misinterpreted, so that bad images are falsely interpreted as good, and we describe how to obtain valid measurements.
Sharpness, which is measured by MTF (Modulation Transfer Function) curves, is frequently summarized by MTF50 (the spatial frequency where MTF falls to half its low frequency value).
High-frequency flickering light sources such as pulse-width modulated LEDs can cause image sensors to record incorrect levels. We describe a model with a loose set of assumptions (encompassing multi-exposure HDR schemes) which can be used to define the Flicker Signal, a continuous function of time based on the phase relationship between the light source and exposure window. (more…)
Camera-based advanced driver-assistance systems (ADAS) require the mapping from image coordinates into world coordinates to be known. The process of computing that mapping is geometric calibration. This paper provides a series of tests that may be used to assess the goodness of the geometric calibration
Shannon information capacity, which can be expressed as bits per pixel or megabits per image, is an excellent figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems.
