Last week Pasha Melnyk defended his PhD dissertation, entitled “Biologically inspired composite image sensor for deep field target tracking.”

Pasha was interested in the problem of deep field tracking, or more specifically, he was interested in using image sensors to track objects from when they are very far from the observer all the way into the near field, when they are close to the observer. Pasha proposed a system in which multiple image sensors of different focal lengths create a composite image sensor that can achieve this type of tracking. However, he then ran into the problem of how to recognize and track objects in this new composite image. Will objects have different characteristics as they move through space and get picked up by different parts of the composite sensor? Pasha found an elegant solution to this problem. He described the composite image by nesting the log polar representations of individual cameras. One result is that objects do not significantly change shape as they are tracked by the multiple cameras.

Pasha successfully applied his idea to the problem of vehicle tracking. He was able to track vehicles from several hundred meters and then capture license plates as the vehicles drove by. The videos of this were really impressive.

Great job Pasha (and Rich Messner, Pasha’s advisor).

Andrew Kun

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