Spectral imaging systems use information from the entire electromagnetic spectrum to provide digital images with much greater information per pixel than traditional cameras.
Feature spaces in a spectral imaging system are vectors that numerically represent an object's characteristics.
Researchers at the Air Force Institute of Technology (AFIT) in US have developed a novel two-dimensional feature space which uses the spectral absorption characteristics of melanin, hemoglobin and water to better characterise human skin.
This would cut the overall cost of hyperspectral-based search and rescue systems by a factor of seven.
"The study represents a crossroads between physics and statistical pattern recognition," said Michael J Mendenhall, assistant professor at AFIT.
"The features were designed based on an understanding of the physics behind skin's spectral shape, but in such a way that the features separated skin and non-skin pixels in order to make the pattern recognition portion of the problem more effective," said Mendenhall.
Many current image recognition programmes employ hyperspectral imaging systems, which allow engineers to search for a wide variety of objects - exoplanets, oil wells, or human skin, to name a few - by looking for specific "fingerprints" in the electromagnetic spectrum.
However, the involved image acquisition and post-processing are typically too slow for live search and rescue operations.
The researchers used their skin detection and false alarm suppression feature space to design an application-specific optical system using three framing cameras.
Their skin detection solution can be implemented with less expensive technology capable of live video frame rates.
The study was published in the journal Applied Optics.
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