Measuring a detection sensitivity metric for non-shift invariant computational imaging systems (Conference Presentation)

Bradley L. Preece; David P. Haefner; George Nehmetallah

doi:10.1117/12.2307829

15 May 2018 Measuring a detection sensitivity metric for non-shift invariant computational imaging systems (Conference Presentation)

Bradley L. Preece, David P. Haefner, George Nehmetallah

Author Affiliations +

Proceedings Volume 10669, Computational Imaging III; 106690V (2018) https://doi.org/10.1117/12.2307829
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

In the modern tactical imaging environment, new computational imaging (CI) systems and algorithms are being used to improve speed and accuracy for detection tasks. Therefore, a measurement technique is needed to predict the performance of complex non-shift invariant EO/IR imaging systems, including CI systems. Detection performance of traditional imaging systems can be modeled using current system metrics and measurements such as Modulation Transfer Function (MTF), Signal to Noise (SNR), and instantaneous Field of View (iFOV). In this correspondence, we propose a technique to experimentally measure a detection sensitivity metric for non-traditional CI systems. The detection sensitivity metric predicts the upper bound of linear algorithm performance though evaluation of a matched filter. The experimental results are compared with theoretical expected values though the Night Vision Integrated Performance Model (NV-IPM). Additionally, we demonstrate the experimental results for a variety of imaging systems (IR, visible, and color), target sizes and orientations, as well as SNR values. Our results demonstrate how this detection sensitivity metric can be measured to provide additional insight into the final system performance.

Conference Presentation

Citation Download Citation

Bradley L. Preece, David P. Haefner, and George Nehmetallah "Measuring a detection sensitivity metric for non-shift invariant computational imaging systems (Conference Presentation)", Proc. SPIE 10669, Computational Imaging III, 106690V (15 May 2018); https://doi.org/10.1117/12.2307829

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