Invisible radiation imaging provides important insights into evidence no1n1ally beyond the visual experience of investigators. Reflected ulu·aviolet (W) photography has classically been used for recording bite marks, bmises, car panel damage and fingerprints. The recent application of UV digital inlaging potentially provides many advantages for forensic investigation as images can be viewed in real time at a crime scene. potentially enabling efficient collection of critical evidence that previously went unseen. However, since UV imaging collects data that is beyond our nonnaJ frame of reference for interpreting results, it is impo11ant that robust methodologie;. can be applied to quantify relative reflectance from different elemem.s ofa potential crime scene. We discuss the dynamics of the non·linear relationships between reflected radiation and the response of commercial grade inlage sensors that are typical in forensic practice, and how die implementation of image processing algorithms based on non-linear functions enables the recovery of robust linearized data for the precise quantification of reflectance in a scene. We demonstrate the application of this process with both a typical (fingerprint) and novel (material identification based on its reflective properties) problem for forensic imaging, and discuss how this linearized process will allow for the accurate docmnentation of reflected UV imaging as evidence in court proceedings.
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