A West Virginia University engineering professor has won a prestigious grant from the Air Force Office of Scientific Research for work that can help model and classify distant objects and their features quicker than ever before.

Christian is shown on right.

John Christian, assistant professor in the Statler College of Engineering and Mineral Resources, is among 57 scientists who will receive a portion of $16.6 million in total grants through the Air Force’s Young Investigator Research Program. The funding will be awarded over a three-year period.

Whether it is spacecraft attempting to rendezvous and dock with another spacecraft or a ground telescope tracking an asteroid far away from Earth, the cameras that gather information about these objects have limitations.

At long ranges, cameras can have a difficult time picking up details about objects or their features. These low-resolution images show partially resolved objects, which Christian likens to blurry objects that you would see in your home if you removed your glasses.

“Humans have trained themselves to identify partially resolved objects based on very little data,” Christian explains. “For instance, even if your vision was blurry you could distinguish your cat from your dog based on the general shape and size. You can’t pick out all of the detail, but based on information that you do have your brain eliminates categories of objects that don’t fit the parameters and deduces what the object is.”

The tricky part is teaching a computer to do the same thing.

Through applied mathematics, Christian is developing computer software that can make guesses about what a partially resolved object actually is. His research group will then go one step further. Using a lot of images taken over time, they hope to be able to produce three-dimensional models of the object at a much higher resolution than what the camera provides by itself.

“By doing this, we will improve the amount of information that we can gather about a distant object and we can do it much faster,” Christian says. “We don’t have to wait for the object to get closer in order to identify it or to understand its shape.”

He went on to say that the idea is quite general, and many of the same ideas that work on entire objects are also likely to work on individual features on an object.

Christian also says that this concept could be applied in any vision-based navigation application such as robots, aerial vehicles and self-driving cars.

The AFOSR Young Investigator Program is open to scientists and engineers at research institutions across the United States who received Ph.D. or equivalent degrees in the past five years and who show exceptional ability and promise for conducting basic research.

“We are delighted with this prestigious Air Force Office of Scientific Research award to Dr. Christian,” said Gene Cilento, Glen H. Hiner Dean of the Statler College. “He has demonstrated significant potential for impressive career development. I also believe his enthusiasm and entrepreneurialism will greatly benefit the growth and recognition of our Statler College educational and research programs for many years to come.”

“We have been recruiting very highly qualified faculty and they are being mentored using a connect-collaborate-innovate approach that we have developed,” said Pradeep Fulay, associate dean for research. “Dr. Christian’s AFOSR award reflects on the quality of faculty we have at WVU.”

Christian’s proposal, “Estimation of Shape and Relative Motion for Partially Resolved Objects in Optically Acquired Imagery,” was one of more than 200 proposals submitted this year.

He received his bachelor’s and master’s degrees in aerospace engineering from the Georgia Institute of Technology and his doctoral degree in aerospace engineering from the University of Texas at Austin. Prior to joining the faculty at WVU, Christian was an engineer in the Guidance, Navigation and Control Autonomous Flight Systems Branch at the NASA Johnson Space Center where he worked on algorithms, sensors and flight software for spacecraft relative navigation.