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Call for Papers Deadlines
» Journal-first (JIST & JPI)
5 April
» Conference
» Presentation Only
26 May
26 Jul
Acceptance Notification
» Conference 30 June 
» Journal-first (JIST & JPI)
7 July 
Final Manuscripts Due
» Journal-first by 12 July
» Conference
23 Aug
Early Registration Ends
31 Aug
Conference Begins
20 Sept

Short Courses

MONDAY 20 SEPTEMBER 13:00 – 15:30 London / 08:00-10:30 New York

Imaging Quality for Automotive and Machine Vision Applications

separate registration fee required

Course Number: SC01
Instructors: Don Williams, Image Science Associates, and Peter Burns, Burns Digital Imaging
Level: Intermediate
Duration: 2 hours
Course Time:
    London: Monday, 20 September, 13:00 – 15:30
    New York: Monday, 20 September, 8:00-10:30

Prerequisites: A general understanding of how digital cameras work and of measurement variation is helpful.

This course enables the attendee to understand:

  • Objective image quality methods for image capture systems.
  • How and why imaging performance methods are being adopted.
  • The difference between imaging performance and image quality.
  • ISO-defined methods, e.g., for color-encoding, image resolution, distortion, and noise.
  • An overview of recent P2020 IEEE standards efforts, and those for machine vision.

This course discusses current initiatives, progress, and challenges to specifying and evaluating imaging performance for automotive applications. Objective image quality methods, as developed for image capture systems are introduced. Several of these methods have been adapted in emerging standards for, example, automotive (ADAS) and machine-vision applications. Most efforts rely on several ISO-defined methods, such as for color-encoding, image resolution, distortion, and noise. While several measurement protocols are similar, the image quality needs can differ. For example, machine vision often emphasizes detector signal and noise characteristics, however the IEEE P2020 automotive imaging initiatives include attributes due to optical and video performance (e.g., distortion and motion artifacts).

Intended Audience
Those developing or managing projects for the selection and use of digital imaging technology for automotive applications.

Peter Burns is a consultant working in imaging system evaluation, modeling, and image processing. Previously he worked for Carestream Health, Xerox, and Eastman Kodak. A frequent instructor and speaker at technical conferences, he has contributed to several imaging standards. He has taught imaging courses for clients and universities for many years. Dr. Burns is an inventor for 22 US patents, and author of 75+ technical publications.

Don Williams, founder of Image Science Associates, was with Kodak Research Laboratories. His work focuses on quantitative signal and noise performance metrics for digital capture imaging devices and imaging fidelity issues. He co-leads the TC 42 standardization efforts on digital print and film scanner resolution (ISO 16067-1, ISO 16067-2), scanner dynamic range (ISO 21550), and is the editor for the second edition to digital camera resolution (ISO 12233). 

16:00-18:30 London / 11:00-13:30 New York

Optics and Hardware Calibration of Compact Camera Modules for AR/VR, Automotive, and Machine Vision Applications

separate registration fee required

Course Number: SC02
Uwe Artmann, Image Engineering GmbH & Co. KG, and Kevin J. Matherson, Microsoft Corporation
Level: Intermediate
Duration: 2 Hours
Course Time:
    London:  Monday, 20 September, 16:00-18:30
    New York:  Monday, 20 September, 11:00-13:30


This course enables the attendee to:

  • Understand the design of optics used in compact camera modules.
  • Understand the difficulties in minimizing sensor and camera modules.
  • Assess the need for per unit camera calibrations in compact camera modules.
  • Determine camera spectral sensitivities.
  • Understand depth of field/depth of focus.
  • Understand autofocus actuators and why per unit calibrations are required.
  • Learn how to perform the various calibrations typically done in compact camera modules (intrinsic calibration, distortion calibration, relative illumination, color shading, spectral calibrations, gain, actuator variability, etc.).
  • Compare hardware tradeoffs such as temperature variation, its impact on calibration, and overall influence on final quality.

Digital and mobile imaging camera and system performance is determined by a combination of sensor characteristics, lens characteristics, and image processing algorithms. Smaller pixels, smaller optics, smaller modules, and lower cost result in more part-to-part variation driving the need for calibration to maintain good image quality. This course provides an overview of issues associated with compact imaging modules used in mobile and digital imaging. The course covers optics, sensors, actuators, camera modules, and the camera calibrations typically performed to mitigate issues associated with production variation of lenses, sensor, and autofocus actuators.

Intended Audience
People involved in the design and image quality of digital cameras, mobile cameras, and scanners. Technical staff of manufacturers and managers of digital imaging projects, as well as journalists and students studying image technology.

Uwe Artmann studied photo technology at the University of Applied Sciences in Cologne following an apprenticeship as a photographer and finished with the German 'Diploma Engineer'. He is now the CTO at Image Engineering, an independent test lab for imaging devices and manufacturer of all kinds of test equipment for these devices. His special interest is the influence of noise reduction on image quality and MTF measurement in general.

Kevin Matherson is a director of optical engineering at Microsoft Corporation working on advanced optical technologies for AR/VR, machine vision, and consumer products. Prior to Microsoft, he participated in the design and development of compact cameras at HP and has more than 20 years of experience developing miniature cameras for consumer products. His primary research interests focus on sensor characterization, optical system design and analysis, and the optimization of camera image quality. Matherson holds a Masters and PhD in optical sciences from the University of Arizona.

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