Using canonical shapes (images) for camera characterization

David P. Haefner; Stephen D. Burks; Joshua M. Doe

doi:10.1117/12.2618292

27 May 2022 Using canonical shapes (images) for camera characterization

David P. Haefner, Stephen D. Burks, Joshua M. Doe

Proceedings Volume 12106, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIII; 1210605 (2022) https://doi.org/10.1117/12.2618292
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

An accurate prediction of the number of pixels on a target is critical in modeling the performance of a cameras ability to perform a task. This requires an accurate knowledge of the angle subtended by a pixel of interest, which can be calculated from a specification sheet or lens prescription. When such information is not available, it can be retrieved through a measurement of a known sized target at a known distance. In this correspondence, we utilize canonical images (ideal simple functions) together with non-linear optimization to provide sub-pixel target localization. This allows for accurate and repeatable measurement of the angular sampling of a camera. Additionally, the use of well-defined shapes and accurate location determination can be used to determine the blur, rotation, motion, contrast, distortion, and other camera metrics.

Conference Presentation

Citation Download Citation

David P. Haefner, Stephen D. Burks, and Joshua M. Doe "Using canonical shapes (images) for camera characterization", Proc. SPIE 12106, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXXIII, 1210605 (27 May 2022); https://doi.org/10.1117/12.2618292

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