Light processing improves robotic sensing, study finds

Date:
September 14, 2020
Source:
U.S. Army Research Laboratory
Summary:
A team of researchers uncovered how the human brain processes
bright and contrasting light, which they say is a key to improving
robotic sensing and enabling autonomous agents to team with humans.



FULL STORY ==========================================================================
A team of Army researchers uncovered how the human brain processes
bright and contrasting light, which they say is a key to improving
robotic sensing and enabling autonomous agents to team with humans.


==========================================================================
To enable developments in autonomy, a top Army priority, machine sensing
must be resilient across changing environments, researchers said.

"When we develop machine vision algorithms, real-world images are usually compressed to a narrower range, as a cellphone camera does, in a process
called tone mapping," said Andre Harrison, a researcher at the U.S. Army
Combat Capabilities Development Command's Army Research Laboratory. "This
can contribute to the brittleness of machine vision algorithms because
they are based on artificial images that don't quite match the patterns
we see in the real world." By developing a new system with 100,000-to-1 display capability, the team discovered the brain's computations, under
more real-world conditions, so they could build biological resilience
into sensors, Harrison said.

Current vision algorithms are based on human and animal studies with
computer monitors, which have a limited range in luminance of about
100-to-1, the ratio between the brightest and darkest pixels. In the
real world, that variation could be a ratio of 100,000-to-1, a condition
called high dynamic range, or HDR.

"Changes and significant variations in light can challenge Army systems -
- drones flying under a forest canopy could be confused by reflectance
changes when wind blows through the leaves, or autonomous vehicles driving
on rough terrain might not recognize potholes or other obstacles because
the lighting conditions are slightly different from those on which their
vision algorithms were trained," said Army researcher Dr. Chou Po Hung.



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The research team sought to understand how the brain automatically
takes the 100,000-to-1 input from the real world and compresses it to a narrower range, which enables humans to interpret shape. The team studied
early visual processing under HDR, examining how simple features like
HDR luminance and edges interact, as a way to uncover the underlying
brain mechanisms.

"The brain has more than 30 visual areas, and we still have only a
rudimentary understanding of how these areas process the eye's image
into an understanding of 3D shape," Hung said. "Our results with HDR
luminance studies, based on human behavior and scalp recordings, show just
how little we truly know about how to bridge the gap from laboratory to real-world environments. But, these findings break us out of that box,
showing that our previous assumptions from standard computer monitors
have limited ability to generalize to the real world, and they reveal principles that can guide our modeling toward the correct mechanisms."
The Journal of Vision published the team's research findings, Abrupt
darkening under high dynamic range (HDR) luminance invokes facilitation
for high contrast targets and grouping by luminance similarity.

Researchers said the discovery of how light and contrast edges interact
in the brain's visual representation will help improve the effectiveness
of algorithms for reconstructing the true 3D world under real-world
luminance, by correcting for ambiguities that are unavoidable when
estimating 3D shape from 2D information.

"Through millions of years of evolution, our brains have evolved effective shortcuts for reconstructing 3D from 2D information," Hung said. "It's a decades-old problem that continues to challenge machine vision scientists,
even with the recent advances in AI." In addition to vision for autonomy,
this discovery will also be helpful to develop other AI-enabled devices
such as radar and remote speech understanding that depend on sensing
across wide dynamic ranges.

With their results, the researchers are working with partners in academia
to develop computational models, specifically with spiking neurons that
may have advantages for both HDR computation and for more power-efficient vision processing -- both important considerations for low-powered drones.

"The issue of dynamic range is not just a sensing problem," Hung said. "It
may also be a more general problem in brain computation because individual neurons have tens of thousands of inputs. How do you build algorithms
and architectures that can listen to the right inputs across different contexts? We hope that, by working on this problem at a sensory level,
we can confirm that we are on the right track, so that we can have the
right tools when we build more complex AIs."

========================================================================== Story Source: Materials provided by U.S._Army_Research_Laboratory. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Chou P. Hung, Chloe Callahan-Flintoft, Paul D. Fedele, Kim
F. Fluitt,
Onyekachi Odoemene, Anthony J. Walker, Andre V. Harrison, Barry D.

Vaughan, Matthew S. Jaswa, Min Wei. Abrupt darkening under high
dynamic range (HDR) luminance invokes facilitation for high-contrast
targets and grouping by luminance similarity. Journal of Vision,
2020; 20 (7): 9 DOI: 10.1167/jov.20.7.9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/09/200914112153.htm

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