Date of Award
Master of Science
Department of Electrical and Computer Engineering
Jeffrey D. Clark, PhD.
Many spectral detection algorithms require precise ground truth measurements that are hand-selected in the image to apply radiometric calibration, converting image pixels into estimated reflectance vectors. That process is impractical for mobile, real-time hyperspectral target detection systems, which cannot empirically derive a pixel-to-reflectance relationship from objects in the image. Implementing automatic target recognition on high-speed snapshot hyperspectral cameras requires the ability to spectrally detect targets without performing radiometric calibration. This thesis demonstrates human skin detection on hyperspectral data collected at a high frame rate without using calibration panels, even as the illumination in the scene changes. Compared to an established skin detection method that requires calibration panels, the illumination-invariant methods in this thesis achieve nearly as good detection performance in sunny scenes and superior detection performance in cloudy scenes.
DTIC Accession Number
Beisley, Andrew P., "Spectral Detection of Human Skin in VIS-SWIR Hyperspectral Imagery without Radiometric Calibration" (2012). Theses and Dissertations. 1079.