BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Imatest - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Imatest
X-ORIGINAL-URL:https://www.imatest.com
X-WR-CALDESC:Events for Imatest
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Denver
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20230312T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20231105T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20240310T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20241103T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20250309T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20251102T080000
END:STANDARD
END:VTIMEZONE
BEGIN:VTIMEZONE
TZID:Europe/Berlin
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20230326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20231029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20241016T110000
DTEND;TZID=America/Denver:20241016T120000
DTSTAMP:20260522T111837
CREATED:20241003T104618Z
LAST-MODIFIED:20241003T110000Z
UID:10000004-1729076400-1729080000@www.imatest.com
SUMMARY:Europe/Americas Office Hour: Correlating image quality with machine vision performance
DESCRIPTION: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 with 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. \nOffice hours include selected topics but are not limited to them. Ask any questions you like when you register or during the call. We’ll try to answer all questions that are submitted if time allows. Office hours are usually held on the third Wednesday of each month at 11:00 am MT for Europe and the Americas and the fourth Wednesday at 7:00 pm MT (9:00 am the next day in China) for Asia and the Americas. View the schedule of upcoming office hours here. \n\nClick here to register for the Office Hour \nRelated Links\n\nCorrelating the Performance of Computer Vision Algorithms with Objective Image Quality Metrics\nImage information metrics: Information Capacity and More
URL:https://www.imatest.com/event/office-hour-eu-am-correlating-image-quality-with-machine-vision-performance/
LOCATION:Zoom Office Hour Europe/Americas
CATEGORIES:Office Hour
ATTACH;FMTTYPE=image/png:https://www.imatest.com/wp-content/uploads/2022/06/Faces_Found.png
ORGANIZER;CN="Imatest":MAILTO:info@imatest.com
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20241008T100000
DTEND;TZID=Europe/Berlin:20241010T164500
DTSTAMP:20260522T111837
CREATED:20241002T131616Z
LAST-MODIFIED:20241028T151513Z
UID:10000002-1728381600-1728578700@www.imatest.com
SUMMARY:Imatest Presented at AutoSens Europe 2024
DESCRIPTION:Imatest attended AutoSens Europe on October 8-10\, 2024 in Barcelona\, Spain. We presented our advanced testing methods and algorithms for validating the quality of automotive cameras. \n  Watch On Demand AutoSens Europe Website \nVirtually Visit Imatest’s Booth\n\n			\n				\n					\n				\n			\n		\nRelated Products \nCalibrated Reference Camera for Camera Monitor System (CMS) testing with Leader SFR-Fit\nLeader SFR-Fit MTF Measurement Software\nImatest Spectral Illuminance Color Sensor\nSFRreg Field Ruggedized Target\nISO 12233 Edge SFR (eSFR) Inkjet chart \nEvent Photos\n\nTutorial by Norman Koren\n\n\nInformation Metrics for Performance and Optimization of Machine Vision Systems.\nNorman Koren\nFounder / CTO \nWe discuss the mathematical background and practical measurement techniques for a new set of image quality metrics that promise to be superior to MTF (sharpness) and noise as predictors of machine vision performance. The new metrics include Noise Equivalent Quanta (NEQ)\, Ideal Observer SNR (SNRi)\, and Edge location standard deviation (Edge σ). They can be measured from slanted edges as well as Siemens star and dead leaves (spilled coins) test chart images.¬ We cover \n\nthe origin and significance of information capacity\, and its use as a fundamental metric for qualifying cameras\,\nthe effect on measurements of common types of image processing\, especially the bilateral filters found in most JPEG images\,\nthe distinction between total information capacity and information capacity for contrast-limited patterns (typically 4:1 or 60% Michelson contrast slanted edges)\, which can be measured for a wide range of exposures\,\nobtaining the metrics for object detection (SNRi) and the object location (Edge σ)\,\nthe effects of sharpening and lowpass (noise-reduction) filtering\, showing how excessive sharpening degrades performance\,\ndesign of matched filters to optimize SNRi and Edge σ for specific tasks\, and tradeoffs for multiple tasks\nprogress in correlating the new metrics with standard machine vison performance metrics\, such as Mean Average Precision (mAP) and Intersection over Union (IoU)\, which are related to object detection and location.\n\n\n\nSee also Image Information Metrics. \nVisit Imatest’s Booth #334 for Demonstrations\n \nLeader SFR-Fit camera resolution measurement software 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. \nCalibrated Reference Camera for Camera Monitor System (CMS) testing with Leader SFR-Fit  \nBy using SFR-Fit Ver2.4 and a calibrated reference camera\, you can measure the MTF of a CMS electronic mirror. \n\nSFRreg Field Ruggedized Target \n \n  Watch On Demand \nPrevious Event: AutoSens USA 2024
URL:https://www.imatest.com/event/autosens-europe-2024/
LOCATION:Palau De Congressos Barcelona\, Avda. Diagonal\, 661*671\, Barcelona\, Les Corts\, 08028\, Spain
ORGANIZER;CN="AutoSens":MAILTO:info@sense-media.com
END:VEVENT
END:VCALENDAR