  Both humans and computational methods struggle to discriminate the depths of
objects hidden beneath foliage. However, such discrimination becomes feasible
when we combine computational optical synthetic aperture sensing with the human
ability to fuse stereoscopic images. For object identification tasks, as
required in search and rescue, wildlife observation, surveillance, and early
wildfire detection, depth assists in differentiating true from false findings,
such as people, animals, or vehicles vs. sun-heated patches at the ground level
or in the tree crowns, or ground fires vs. tree trunks. We used video captured
by a drone above dense woodland to test users' ability to discriminate depth.
We found that this is impossible when viewing monoscopic video and relying on
motion parallax. The same was true with stereoscopic video because of the
occlusions caused by foliage. However, when synthetic aperture sensing was used
to reduce occlusions and disparity-scaled stereoscopic video was presented,
whereas computational (stereoscopic matching) methods were unsuccessful, human
observers successfully discriminated depth. This shows the potential of systems
which exploit the synergy between computational methods and human vision to
perform tasks that neither can perform alone.
