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DTSTART;TZID=America/Los_Angeles:20260301T140000
DTEND;TZID=America/Los_Angeles:20260301T170000
DTSTAMP:20260404T114707
CREATED:20251229T230012Z
LAST-MODIFIED:20251231T174020Z
UID:10000012-1772373600-1772384400@www.imatest.com
SUMMARY:EI2026 Short Course: Camera Simulation for Predicting Information Metrics and Machine Vision Performance
DESCRIPTION:Instructor: Norman L. Koren\nWhen: Sunday\, March 1st 2:00 PM – 5:00 PM PST \nBenefits: \n\nUnderstand the fundamentals of information capacity and how it relates to conventional SFR and noise measurements\,\nUnderstand the information metrics (information capacity and others derived from information theory)\, and learn how they relate to system performance\,\nLearn the fundamentals of camera system simulation\, including preparing the image (typically a test chart)\, determining the lens degradations\, image sensor noise model\, and Image Signal Processing (ISP)\,\nLearn how to determine the effects of each system component or image processing step on the system performance.\n\nCourse description: \nThis course introduces the basics of information theory\, shows why the key information metrics (information capacity and SNRi) are superior to traditional metrics such as sharpness (SFR) and noise for characterizing camera performance\, then show how to calculate them from test chart images.  \nNext\, the course describes the camera performance simulator\, including \n\nCreating input images (usually test targets)\,\nSimulating lens degradations\,\nModeling the image sensor noise and ISP (Image Signal Processing)\,\nDisplaying results\, including the standard performance metrics and new information metrics.\n\nThis course will discuss C4\, the information capacity measured directly from ISO 12233-compliant 4:1 contrast slanted edges\, and show how it can characterize performance over a range of illumination. Finally\, it includes a discussion work to correlate information metrics with machine vision performance.\n \nIntended Audience: Engineers who are tasked with designing camera systems for a variety of applications\, often in the automotive and medical industries. They typically have degrees in sciences such as physics or in electrical or mechanical engineering\, but may be new to image science. Ideally\, they should have some experience in imaging system design\, though the course will accommodate beginners with limited engineering experience. \n \nNorman Koren became interested in photography while growing up near the George Eastman Museum in Rochester\, NY. He received his BA in physics from Brown University (1965) and his masters in physics from Wayne State University (1969)\, then worked in the computer storage industry simulating digital magnetic recording systems and channels for disk and tape drives from 1967-2001. He founded Imatest LLC in 2003 to develop software and test charts to measure the quality of digital imaging systems. \nEI2026 Details Registration 
URL:https://www.imatest.com/event/ei2026-short-course-camera-simulation-info-metrics/
LOCATION:Hyatt Regency San Francisco Airport\, 1333 Bayshore Highway\, Burlingame\, CA\, 94010\, United States
CATEGORIES:Training Course
ATTACH;FMTTYPE=image/png:https://www.imatest.com/wp-content/uploads/2025/12/shortcourse_2026_teaser.png
ORGANIZER;CN="IS&T%3A Society for Imaging Science and Technology":MAILTO:ei@imaging.org
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20260301
DTEND;VALUE=DATE:20260306
DTSTAMP:20260404T114707
CREATED:20251222T202856Z
LAST-MODIFIED:20260302T162910Z
UID:10000009-1772323200-1772755199@www.imatest.com
SUMMARY:Electronic Imaging 2026 (EI2026)
DESCRIPTION:Where Industry and Academia Meet to Advance Imaging\nJoin us in person for EI 2026 at the Hyatt Regency San Francisco Airport in Burlingame\, California! \nEI 2026 offers: \n\nExciting Symposium Plenaries and Conference Keynotes.\nA rich technical program of oral talks\, interactive poster papers\, a demonstration session\, and multiple opportunities to network.\nAn industry exhibit and conference lunches.\nA robust short course program.\nA dynamic environment to interact with colleagues from industry and academia across the globe.\n\nElectronic Imaging 2026 brings together multiple technical conferences covering all aspects of imaging. \nLearn More Registration  \n\nExhibition\nThe Exhibition will be held on Tuesday March 3rd 10AM-7PM\, and Wednesday March 4th 10AM-3:30PM.  Stop by our booth to chat with our imaging scientists. \n\nImatest is presenting:\n\nMarch 1: Short Course: Information Metrics for Machine Vision\nMarch 3 11:00: Paper: Toward Fair and Accurate Camera Testing: Validation of Skin Tone Test Charts with Real Human Data\nMarch 3\, 3:30: Paper: Image Sensor Noise model for Image System Simulation \nMarch 3\, 3:30: Panel session: road markings and signage for autonomous vehicles.\nMarch 4\, 9:30: Paper: Centroid to Low-Pass Edge Fitting in ISO 12233 eSFR: Accuracy and Impact on Digital Imaging Information Metrics\nMarch 4\, 3:30: Paper: Information-based Dynamic Range\nMarch 4\, 4:30: Paper: A method for calculating NIR bandpass-adjusted Optical Densities for better matching common standard test chart specifications.\n \n\n\nShort Course: Camera Simulation for Predicting Information Metrics and Machine Vision Performance\nInstructor: Norman L. Koren\nWhen: Sunday\, March 1st 2:00 PM – 5:00 PM PST \nBenefits: \n\nUnderstand the fundamentals of information capacity and how it relates to conventional SFR and noise measurements\,\nunderstand the information metrics (information capacity and others derived from information theory)\, and have a feeling of how it relates to system performance\nlearn about the latest work being done to correlate the information metrics with system performance\nLearn the fundamentals of system simulation\, including preparing the image (typically a test chart)\, determining the lens degradations and image sensor noise model\nKnow how to determine the effects of each system component or image processing step on the system performance.\n\nShow More\nCourse description: \nThis course introduces the basics of information theory\, shows why the key information metrics (information capacity and SNRi) are superior to traditional metrics such as sharpness (SFR) and noise for characterizing camera performance\, then show how to calculate them from test chart images.  \nNext\, the course describes the camera performance simulator\, including \n\ncreating input images (usually test targets)\,\nSimulating lens degradations\,\nRhe image sensor noise model\,\nISP (Image Signal Processing)\,\nResults\, including the standard and new information metrics.\n\nThis course will discuss C4\, the information capacity measured directly from ISO 12233-compliant 4:1 contrast slanted edges\, and show how it characterizes performance over a range of illumination. Finally\, it includes a discussion work to correlate information metrics with machine vision performance.\n \nIntended Audience: Engineers who are tasked with designing camera systems for a variety of applications\, often in the automotive and medical industries. They typically have degrees in sciences such as physics or in electrical or mechanical engineering\, but may not be well-versed in image science. Ideally\, they should have some experience in imaging system design\, though the course will accommodate beginners with limited engineering experience. \nNorman Koren became interested in photography while growing up near the George Eastman House photographic museum in Rochester\, NY. He received his BA in physics from Brown University (1965) and his masters in physics from Wayne State University (1969)\, then worked in the computer storage industry simulating digital magnetic recording systems and channels for disk and tape drives from 1967-2001. He founded Imatest LLC in 2003 to develop software and test charts to measure the quality of digital imaging systems. \n\n\nScreenshot \n“Toward Fair and Accurate Camera Testing: Validation of Skin Tone Test Charts with Real Human Data”\nMegan Borek\, Amelia Limbocker\, Ellis Monk \nSession: IQSP Skin Tone Capture and Image Quality I\nDate: March 3rd\nTime: 11:00 AM\nDuration: 20 minutes \nAccurate reproduction of diverse skin tones remains a persistent challenge for consumer cameras\, with shortcomings in automatic exposure and white balance often leading to biased results. To address this\, we are developing skin tone test charts designed to better represent real human skin across the full range of the Monk Skin Tone Scale. This work validates these charts using real human data collected through multiple methods. First\, survey responses capture user perspectives on how their skin tones are represented in smartphone images and what they expect from their devices. Second\, controlled photographic sessions with participants across the scale provide image data under varied lighting conditions and multiple exposure strategies\, including gray card\, spot metering\, and bracketing\, alongside participant-selected preferred \nmeasurements from skin regions supplement the imaging data with objective ground truth. Together\, these datasets allow us to evaluate and tune test chart behavior so that color and exposure responses align more closely with real skin. This validation represents a step toward fairer\, more accurate camera testing standards\, with implication for consumer photography as well as applications in medical\, automotive\, and security images. \n\n \n“Image Sensor Noise model for Image System Simulation”\nNorman L. Koren \nSession: IQSP\nDate: March 3rd\nTime: 3:30 PM\nDuration: 20 minutes \nWe present an image sensor noise model\, which is part of a complete image system simulation that includes image generation\, lens degradations\, and ISP (Image Signal Processing)\, and can produce classic measurements (SFR\, noise\, etc.) as well as the new information metrics (information capacity\, SNRi\, etc.). \nThe noise model is derived from a classic Photon Transfer Curve (PTC) obtained from one or at most two raw (undemosaiced) images of a high dynamic range grayscale test chart. Image sensor noise is composed of three factors. \nShow More\n\nDark noise\, which includes electronic noise\, dark current noise\, and DSNU fixed-pattern noise. It is independent of amplitude.\nPhoton shot noise\, which varies with the square root of the amplitude\, and\nPRNU fixed-pattern noise\, which varies linearly with amplitude.\n\nThe coefficients for the three factors are determined using a Levenberg Marquardt optimization that provides an extremely close fit between the data to the measured PTC. The coefficients can also be derived from EMVA 1288 measurements\, which are more detailed\, but require a large number of\nimages to acquire. \nWe show how the model can predict performance over a wide range of conditions\, most importantly\, for low light.\n\nPanel session: road markings and signage for autonomous vehicles.\nPaul Romanczyk PhD \nSession: AVM\nDate: March 3rd\nTime: 3:30 PM  \nDiscussion relating to colors and spectral properties of various road elements\, such as pavement markings and traffic signs\, and how their properties influence the detectability by machine vision systems. \n\n“From Centroid to Low-Pass Edge Fitting in ISO 12233 eSFR: Accuracy and Impact on Digital Imaging Information Metrics”\nSarah Kerr \nSession: IQSP\nDate: March 4\nTime: 9:30 AM\nDuration: 20 minutes \nEdge localization estimation methods play a critical role in ISO 12233 eSFR analysis\, influencing both sharpness results and downstream information capacity metrics. This paper evaluates the accuracy of the standard centroid method relative to a low-pass filter approach across cameras and ISO ranges. Localization errors are benchmarked against low-noise ground truth\, and their propagation to eSFR results\, information capacity\, and SNRi metrics is quantified. Findings show that centroid fitting introduces angular bias under noise\, leading to blurrier effective responses\, while low-pass filtering maintains robust accuracy. These results highlight an underexplored source of error in standards-based image quality analysis and provide a foundation for improved methods. The approach will be extended toward matched filtering\, enabling a closer alignment between edge analysis\, information-theoretic models\, and emerging metrics such as those in ISO 23654 (Digital Imaging — Information Metrics). \n\n“Information-based Dynamic Range”\nNorman L. Koren \nSession: AVM\nDate: March 4th\nTime: 3:30\nDuration: 20 minutes \nWe present a new approach to measuring camera dynamic range and low-light performance based on C4 information capacity\, which is measured directly from ISO 12233-standard 4:1 contrast slanted edges. Our initial technique involves photographing a test chart that contains 4:1 slanted edges over an extremely wide range of exposures\, from ½ or ¼ second (where the brighter side of the edge saturates) to 1/2000 or 1/4000 second\, where the image appears nearly black\, but a noisy edge is still present. The major advantages of this method are \n\nDynamic range limits are based on an actual performance metric (C4) rather than SNR\, which is only one of the factors that contributes to camera performance.\nC4 correctly handles performance degradation due to stray light.\n\nWe will discuss new techniques\, still under development\, for facilitating the measurement. \n\n“A method for calculating NIR bandpass-adjusted Optical Densities for better matching common standard test chart specifications.”\nChristian Taylor\, Amelia Limbocker \nSession: AVM\nDate: March 4th\nTime: 4:30 PM\nDuration: 20 minutes \nNear-infrared (NIR) imaging is now prevalent in machine vision\, automotive\, and biomedical applications\, but most step-chart definitions were created for visible imaging. Many standards assume visible lighting conditions and only consider IR-blocking\, so NIR-sensitive and RGB+NIR cameras are not adequately addressed. This leads to charts whose nominal densities don’t produce results as intended. \nShow MoreWe present a camera-matched methodology for designing NIR  test charts whose optical densities (ODs) align with the effective bandpass of a specific camera. First\, we estimate the camera–illumination–optics bandpass by measuring camera spectral responsivity and the measured illuminant spectrum at the sample plane. Next\, we measure transmittance and/or reflectance spectra of candidate chart materials via spectrophotometry and predict their camera-effective ODs as band-integrated log-ratios. We then select step values to meet the target OD values. Validation is performed by imaging the manufactured chart in RAW\, applying linearization\, and comparing measured image OD to predictions and to a reference spectrophotometer integrated over the same band. The framework supports transmission and reflectance charts\, mono and RGB-NIR sensors\, and bands spanning ~780–1100 nm. We report a practical design recipe and guidelines for dynamic-range coverage and repeatability\, enabling camera-aware NIR chart optimization rather than one-size-fits-all designs.
URL:https://www.imatest.com/event/ei2026/
LOCATION:Hyatt Regency San Francisco Airport\, 1333 Bayshore Highway\, Burlingame\, CA\, 94010\, United States
CATEGORIES:Trade Show
ATTACH;FMTTYPE=image/jpeg:https://www.imatest.com/wp-content/uploads/2025/12/ei2026.jpg
ORGANIZER;CN="IS&T%3A Society for Imaging Science and Technology":MAILTO:ei@imaging.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20250205T153000
DTEND;TZID=America/Los_Angeles:20250205T173000
DTSTAMP:20260404T114707
CREATED:20241024T192849Z
LAST-MODIFIED:20241114T130349Z
UID:10000006-1738769400-1738776600@www.imatest.com
SUMMARY:EI2025 Short Course: Information metrics for optimizing Machine Vision systems
DESCRIPTION:Description: A new set of metrics based on information theory promises to be superior to traditional MTF (SFR or sharpness) and noise for predicting machine vision system performance. We will introduce the new information metrics\, which include Noise Equivalent Quanta (NEQ)\, camera information capacity\, Ideal Observer SNR (SNRi – for the quality of object detection)\, and Edge location standard deviation (Edge σ – for the quality of edge location). We will cover the background of the new measurements\, why they are more directly related to object and edge detection than traditional measurements\, how to conveniently obtain them (primarily from standard slanted edge test patterns)\, how to interpret them\, and how to design matched filters for optimum system performance. \nLevel: Intermediate \nLength: 2 hours \nInstructor: Norman Koren\, founder & CTO Imatest LLC \nPrerequisites: Knowledge of basic image quality measurement concepts\, especially MTF (SFR) and noise \nBenefits: \n\nthe history and mathematics of information theory in imaging\,\ndefinitions and interpretations of the information metrics\, which include camera information capacity\, Noise Equivalent Quanta\, Ideal Observer SNR (SNRi – a metric for the quality of object detection)\, and Edge location standard deviation (Edge σ – a metric for the quality of edge location).\nHow to conveniently obtain the information metrics\nthe effect of common types of image processing on metrics\, including uniform sharpening and lowpass filtering (for noise-reduction)\, as well as the nonuniform bilateral filtering found in most camera JPEG images\,\ndesign of matched filters to optimize SNRi and Edge σ\,\nprogress in correlating the new metrics with machine vision performance and developing ISO 23654\n\nIntended Audience: Engineers who design and analyze cameras and imaging systems for automotive\, medical\, security applications\, and more \nFormat: Lecture Primarily lecture\, but about 1/4 to 1/3 of the time will be demonstrations \nKey Words: MTF\, information capacity\, Artificial intelligence\, machine vision. \nRegistration is open – Click Here to register \nElectronic Imaging 2025\, held February 2–6 at the Hyatt Regency San Francisco Airport\, offers a vibrant platform for industry and academia to advance imaging technologies. The event features plenary talks\, keynotes\, technical sessions\, networking opportunities\, an industry exhibit\, and a short course program\, covering all aspects of electronic imaging in a dynamic and collaborative environment. \nLearn more about Electronic Imaging 2025 \nIf you have any questions\, please contact info-metrics@imatest.com.
URL:https://www.imatest.com/event/ei2025-short-course-information-metrics-for-optimizing-machine-vision-systems/
LOCATION:Hyatt Regency San Francisco Airport\, 1333 Bayshore Highway\, Burlingame\, CA\, 94010\, United States
CATEGORIES:Training Course
ATTACH;FMTTYPE=image/jpeg:https://www.imatest.com/wp-content/uploads/2024/04/Information-Metrics.jpg
ORGANIZER;CN="IS&T%3A Society for Imaging Science and Technology":MAILTO:ei@imaging.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Denver:20250202T080000
DTEND;TZID=America/Denver:20250206T170000
DTSTAMP:20260404T114707
CREATED:20241002T133020Z
LAST-MODIFIED:20250313T164502Z
UID:10000003-1738483200-1738861200@www.imatest.com
SUMMARY:Electronic Imaging 2025
DESCRIPTION:Imatest attended Electronic Imaging 2025 in Burlingame CA. \n  \n\n\nFeatured Talk:\nMegan Borek “Improving Image Equity: Representing the diversity of skin tones in photographic test charts for digital camera characterization” (4391)\nSession: Skin Tone Capture and Image Quality I\nDate: 2/5/2025 (Wednesday)\nTime: 11:00 AM – 12:30 PM (Pacific Time)\nDuration: 20 minutes \n\nFeatured Short Course:\nNorman Koren: Information Metrics for Optimizing Machine Vision Systems \nDate: 2/5/2025 (Wednesday)\nTime: 3:30 – 5:30 PM (Pacific Time)\nDuration: 2 hours \n\nAfter the Event:\nOpen House / Happy Hour at the Edmund Optics Solution Center in Cupertino \nDate: 2/6/2025 (Thursday)\nTime: 3:00 – 7:00 PM (Pacific Time)\nDuration: 4 hours
URL:https://www.imatest.com/event/electronic-imaging-2025/
LOCATION:Hyatt Regency San Francisco Airport\, 1333 Bayshore Highway\, Burlingame\, CA\, 94010\, United States
CATEGORIES:Trade Show
ATTACH;FMTTYPE=image/png:https://www.imatest.com/wp-content/uploads/2024/10/EI2025_modified.png
ORGANIZER;CN="IS&T%3A Society for Imaging Science and Technology":MAILTO:ei@imaging.org
END:VEVENT
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