Webinars – Imatest

The market for cameras that produce images for Machine vision (MV) and Artificial Intelligence (AI), in contrast to pictorial images for human vision, is steadily growing. Applications include automotive (driver assistance and autonomous vehicles), robotics, security, and medical imaging systems.

Two questions arise when designing camera systems for such applications.

How best to select (or qualify) cameras for MV/AI applications?
What image processing (ISP or filtering) is optimal?

To answer these questions, we must go beyond standard measurements of sharpness (MTF) and noise and apply metrics derived from information theory, including information capacity and related metrics for object and edge detection.

These metrics are important because Object Recognition (OR), MV, and AI algorithms operate on information, not pixels, making them far better predictors of system performance than MTF or noise.

Imatest has developed highly convenient methods for measuring information capacity and related metrics. The white papers (with varying degrees of detail) describe how the new metrics can be used to select (or qualify) cameras and determine the optimum Image Signal Processing (ISP) for Object Recognition, which is likely to improve the performance of MV and AI algorithms.

White papers

Three November 2023 white papers (probably too many; it may be reduced to two) contain the latest information capacity measurements and results, including Edge SNRi, filter design, and many other significant enhancements over the 23.2 release. They are available in the Imatest 24.1 Pilot Program until Imatest 24.1 is released in spring, 2024.

The three white papers linked below contain the same essential material.
They range from concise and technically lightweight for managers and non-technical readers
to detailed and comprehensive (but challenging to read).

Introduction to Image Information Metrics is the most concise introduction to image information capacity and related metrics, with a minimum number of equations and minimal technical detail. We may shrink it further for use in a brochure for marketing.
Image Information Metrics in Imatest is a concise and readable introduction to image information capacity and related object and edge detection metrics with moderate technical detail.
Image Information Metrics and Applications: Reference (41 pages) has all the equations and technical detail, but it may be difficult to read. It’s the reference for the other documents.

Camera Information Capacity from Siemens Stars (2020) describes a method that is slower and less versatile than the slanted edge, but better for observing the effects of image processing artifacts such as demosaicing, data compression, etc.

Imatest Software Documentation

Information capacity measurements from Slanted edges: Equations and Algorithms (2023) – figures of merit that combine sharpness and noise, conveniently measured from any slanted-edge, including NPS, NEQ, and SNRi. Similar to the white papers, but not updated as frequently.
Information capacity measurements from Slanted edges: Instructions (2023) – instructions on the new calculations.
Improved slanted-edge MTF calculation – We found errors in calculations that depended on MTF²(f)/NPS(f). They were reduced by an improvement in the ISO 12233-based MTF calculation.
Shannon information capacity from Siemens stars
- Meaning – Results – Summary

International Standard (in development)

ISO 23654 began development in early 2023. It is based on ISO 12233 and defines how to calculate information capacity from a slanted edge. It is in the PWD (preliminary working draft) phase.

Call for Participation

We are interested in working with machine vision and computer vision experts who are experienced with studying how image quality metrics correlate with object detection performance in computer vision systems. Please contact infocap@imatest.com if you are interested

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