Imatest is excited to announce we were awarded Best Validation Simulation Tool for our software and charts at AutoSens Brussels 2022. These awards celebrate the best and brightest working at the cutting-edge of innovation in ADAS and autonomous vehicle technology. View the full list of winners here: https://auto-sens.com/events/awards/
Imatest announces new Target Generator Library
We are happy to announce the release of the new Target Generator Library today. The Imatest Target Generator is free software that will facilitate rapid, iterative lens and camera design by enabling simulations that can help a designer make better informed decisions about what components are appropriate in advance of costly prototyping phases.
The new Imatest Target Generator provides virtual chart solutions. Quantitative and qualitative results from applying ray tracing, noise effects, and image signal processing to virtual charts aid engineers in communicating the impact of various lens and camera design parameters. Image quality deficiencies can be observed and mitigated using a virtual chart during the simulation phase. Virtual charts can aid engineers in comparing and resolving discrepancies between designs and builds. (more…)
Correlating the Performance of Computer Vision Algorithms with Objective Image Quality Metrics
by Henry Koren
1. Why care about the quality of your cameras?
The task of computer vision (CV) involves analyzing a stream of images from an imaging device. Some simple applications such as object counting may be less dependent on good camera quality. But for more advanced CV applications where there is limited control of lighting & distance, the quality of your overall vision system will depend on the quality of your camera system. This is increasingly important when an error made by the vision system could lead to a decision that impacts safety. Along with proper optimization of a CV model, ensuring that that model is fed by imagery from a high-quality camera system is critical to maximizing your system’s performance.
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Logarithmic wedges: a superior design
We introduce the logarithmic wedge pattern, which has several advantages over the widely-used hyperbolic wedges found in ISO 12233 (current and older) and eSFR ISO charts. Logarithmic wedges will be available in eSFR ISO charts starting in 2022, and will be supported by Imatest 2021.2+.
The key advantage of logarithmic wedges is that charts with large fmax/fmin ratios work well for systems with a wide range of resolutions, unlike hyperbolic wedges, where large ratios cause high frequencies occupy excessive real estate and low frequencies to become highly compressed. The fmax/fmin ratio of the hyperbolic wedges in the ISO 12233/2017 chart is only 12.5:1 — insufficient for modern high resolution cameras.
Using images of noise to estimate image processing behavior for image quality evaluation
In the 2021 Electronic Imaging conference (held virtually) we presented a paper that introduced the concept of the noise image, based on the understanding that since noise varies over the image surface, noise itself forms an image, and hence can be measured anywhere, not just in flat patches.
You can download the full paper (in the original PDF format) here.
Introducing MTS-RM-NIR
The Imatest Modular Test Stand Reflective Module now integrates with Metaphase NIR lights to allow fast, efficient testing in NIR wavelengths.
Real-time focusing with Imatest Master direct data acquisition
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.
Image Quality Testing for Webcams
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…)
Correcting Misleading Image Quality Measurements
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.
Describing and Sampling the LED Flicker Signal
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…)
Validation Methods for Geometric Camera Calibration
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
Making Dynamic Range Measurements Robust Against Flare Light
Introduction
A camera’s Dynamic Range (DR) is the range of tones in a scene that can be reproduced with adequate contrast and good signal-to-noise ratio (SNR). Camera DR is often limited by flare light, which is stray light in the image, primarily caused by reflections between lens elements. Flare light reduces DR by fogging images; i.e., washing out detail in dark areas. (more…)
P2020 Automotive Engineering Technology May 2019
Imatest attended the P2020 meeting on May 13 and 14, 2019 in Ann Arbor, Michigan. Paul Romancyzk, PhD., Senior Imaging Scientist, and Rob Sumner, Lead Engineer, represented Imatest. Paul co-led the discussion on Color Separation within the Image Quality for Machine Vision subgroup.
Understanding collimator MTF loss using bronze and golden sample testing
by Henry Koren, inspired by Paul Romanczyk, edited by Norman Koren
Not all MTF measurement systems will necessarily provide the same results. The quality of the test target can impact the measurements you obtain. Long distance tests are ideally performed at the hyperfocal distance, where there is enough depth of field to have acceptable focus at infinity. (more…)
Imatest at IEEE P2020
Imatest sent two engineers to the IEEE P2020 Automotive Imaging Quality face-to-face meeting in Dusseldorf, Germany this past February in 2019. The IEEE P2020 standard, which is still in development, aims to define KPIs and test procedures to address the many challenges relevant (and often unique) to automotive imaging. (more…)