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DTSTART;VALUE=DATE:20240513
DTEND;VALUE=DATE:20240514
DTSTAMP:20240513T171054Z
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UID:10000017-1715558400-1715644799@www.imatest.com
SUMMARY:Imatest Presented at AutoSens USA 2024
DESCRIPTION:Leader SFR-Fit MTF Measurement Software\n \nSFR-Fit is camera resolution measurement software that measures MTF (Modulation Transfer Function). MTF indicates spatial frequency characteristics using multiple distortion and uniformity-corrected sine patterns that can be presented across one or more displays. \nConventional MTF measurement methods can have issues with black box camera systems that prevent the acquisition of the RAW or minimally processed image.  If these systems have sharpening enabled\, slanted edges can overstate MTF\, especially at high frequencies. \nThe SFR-Fit measurement system can produce highly accurate measurements of automotive cameras is compatible with ultra-wide-angle lenses. \nLearn More\n  \n\nYou can watch all the Imatest presentations at AutoSens USA 2024\, and many many more by purchasing an AutoSens On-Demand Pass \nImatest AutoSens Talks\nJonathan Phillips\nVP Imaging Science \nCamera Image Quality for ADAS – How Good is Good Enough?\nTuesday May 21st\, 2-5pm \nThis tutorial will provide a detailed understanding of the camera image quality standards and metrics that relate to ADAS (Advanced Driver Assistance Systems). The content will cover key image quality factors (e.g.\, sharpness\, noise\, distortion\, and color) and the metrics that provide quantification of these factors (e.g.\, spatial frequency response\, temporal noise\, camera projection model\, and color error)\, Discussion will include how to determine if key performance indicators (KPIs) are being met and how to relate these to camera system performance. Metrics will include those from the IEEE P2020 standard\, ISO TC42 Photography standards\, and novel image information metrics that relate to machine vision and object recognition. \n\nNorman Koren\nFounder / CTO \nKeynote – New camera quality measurements for optimizing Machine Vision systems\nWednesday May 22nd\, 10am \nWe describe information capacity measurements\, which can be derived from slanted edge\, Siemens star\, and dead leaves images (focusing on slanted edges)\, and why they should be used to qualify cameras for machine vision systems. \nThen we describe two key metrics\, closely related to information capacity\, that are superior to standard sharpness (MTF or SFR) and noise measurements for characterizing camera performance. \n\nobject detection (SNRi)\, and\nobject location (closely related to edge detection).\n\nWe also discuss several topics related to information metrics\, including \n\nthe effects of image processing (raw\, minimal\, uniform\, nonuniform processing)\nhow to design electronic filters\, called matched filters\, for optimizing the key metrics\,\nwhy excessive sharpening (which may look good for human vision) can be harmful for machine vision\,\nan approach to measuring information capacity for contrast-limited patterns (typically 4:1 or 60% Michelson contrast) over a wide range of exposures\, down to dim light.\n\nPaul Romanczyk\nSenior Imaging Scientist \nWhy stray light (flare) is an issue for automotive cameras\n  \nThursday May 23rd\, 11am \nThe push toward autonomous driving is leading to the inclusion of camera systems in cars. The variability of scene content in automotive requires that these cameras be usable across a large dynamic range. Image artifacts and degradations caused by stray light limit the performance of the camera systems.  \nStray-light-induced degradation of image quality will adversely affect both the machine learning/computer vision (ML/CV) algorithmic performance and the human perception of the image. This talk will briefly cover testing methodologies under development for quantifying a camera system’s stray-light performance. \n \n\nSarah Kerr\nImaging Scientist \nPanel discussion: Standards for Automotive Safety: the crucial role of IEEE P2020 Automotive Image Quality Working Group.\nWeds May 22nd\, 5.30 – 6.15pm \nJoin us for a compelling panel discussion highlighting the pivotal role of the IEEE P2020 Automotive Image Quality Working Group. As the ADAS/AV landscape evolves\, the IEEE P2020 Standards have emerged as a linchpin\, establishing vital guidelines and benchmarks to enhance vehicle safety by ensuring reliability and consistency of the imaging systems. Robust standards are paramount for automotive imaging technologies; this panel will outline how the efforts of the working group members have contributed to consistency and reliability in automotive imaging\, from addressing image processing challenges to ensuring cross-system compatibility. \nGain profound insights into the crucial role these standards play in advancing the technological framework\, elevating safety and efficacy\, and shaping the future of autonomous driving through standardized and reliable automotive imaging technologies. \n \nYou can watch all the Imatest presentations and many many more by purchasing an AutoSens USA On-Demand Pass \n\nVirtually visit our last booth at Autosens Brussles 2023
URL:https://www.imatest.com/event/autosens-usa-2024/
LOCATION:CA
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