UCLA researchers have released open source code for a powerful image detection algorithm that could boost fingerprint and facial recognition capabilities.Called the Phase Stretch Transform algorithm, the team behind the solution say it can be adapted to face, fingerprint and iris recognition for high-tech security, as well as in self-driving cars' navigation systems or for inspecting industrial products.The algorithm was developed by a group led by Bahram Jalali, a UCLA professor of electrical engineering and holder of the Northrop-Grumman Chair in Optoelectronics, and senior researcher Mohammad Asghari.Because it was released Github and Matlab File Exchange, researchers can work together to study, use and improve the algorithm.The algorithm, which grew out of UCLA research on a technique called photonic time stretch, is a physics-inspired computational approach to processing images and information. It helps computers see features of objects that aren't visible using standard imaging techniques.For example, it might be used to detect an LED lamp's internal structure, which – using conventional techniques – would be obscured by the brightness of its light, and it can see distant stars that would normally be invisible in astronomical images.
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