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ORNL: Using algorithms to see the world differently

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Cameras see the world differently than humans. Resolution, equipment, lighting, distance and atmospheric conditions can impact how a person interprets objects on a photo. For Sophie Voisin, a software engineer at the Department of Energy’s Oak Ridge National Laboratory, images can reveal what human eyes can’t see, giving a different perspective to understanding how the world changes day to day.

Technically, Voisin’s work at ORNL is all about images — enhancing, improving, analyzing and exploiting high-altitude and low-altitude imagery and full-motion video from drones. Each day, satellites use cameras, referred to as sensors, to view what is happening all over the world. The volume of images captured daily is enormous. This is where Voisin’s true technical passion comes into play.

“I like to code. I like the technical side of programming algorithms to find the sweet spot to apply the latest and greatest research to real problems,” said Voisin. “I can immediately see if changes to my algorithm are applied correctly.”

Maintaining speed, accuracy

Voisin has been nurturing algorithms for the past seven years to process increasingly larger collections of imagery faster. What started out as one small application has grown to 12 projects; she now leads a team of 32 as the primary investigators. The team aims to use software development, scientific research and software engineering to figure out which images show changes to the landscape that may be of interest to the U.S. government. By letting computers sort through the initial mounds of pictures and flag certain ones, analysts can then review and interpret which images actually have value to decision makers.

Image processing is not an easy process. The sheer number of raw images continues to increase as sensors capture more snapshots of the world. Processing each image increases its size two- or threefold. With large file sizes making up huge datasets, high-performance computers offer the best chance of processing images at the speed needed to make relevant decisions. Leveraging the speed of ORNL’s high-performance computers, Voisin’s team also strives to provide high confidence in the accuracy of results.

Throughout the last half decade, Voisin adapted and maintained applications as hardware changed and graphics cards increased in capability. To prevent the algorithm from breaking, Voisin and her team test new hardware before it goes live to ensure continued flow of information.

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