Automated image-based range performance measurement using TOD

Abstract

Image processing (including histogram equalization, local area processing, and edge sharpening) is a key component of practical electro-optical imaging systems. Despite this, the range performance impact of such processing remains difficult to quantify, short of running a full human perception experiment. The primary difficulty is that current analytic range performance models—best exemplified by the Targeting Task Performance (TTP) model—can only account for linear and shift-invariant (LSI) image effects. We present our efforts towards developing a quantitative, image-based range performance metric that does not require LSI assumptions. Our proposed metric is based on a Triangle Orientation Discrimination (TOD) target set and observer task, with automatic scoring accomplished through a simple template correlator. The approach is compatible with both synthetic and real imagery.