collect data are becoming much less expensive
and very efficient. Examples include video,
lidar, radar, sonar, and acoustics or audio
information. Outlined below are three areas
where Vy’s highly reliable shape detection can
add value to existing solutions while opening
the possibility of innovative new solutions.
awareness and an aging population is driving a
6.6 percent increase in the global market for
biopsies. New florescent microscopes are
generating vast amounts of data much of which
has to analyzed manually. A significant
opportunity exists for technology that can
reliably identify, count, and characterize
complex cell structures. Vy’s technology is
designed to work with existing analysis
techniques to solve this problem.
The Image below shows
a cell cluster analyzed with a traditional edge
detector on the left and Vy’s patented CellQuant
technology on the right. Existing analysis
techniques are unable to identify and count
cells that are occluded or clumped together.
Vy’s solution helps researchers increase the
quality and quantity of data available from
slide imagery while substantially reducing the
manual analysis required.
Video Search and Analysis
Reliably searching videos for objects and
activities of interest is a huge problem for law
enforcement, industry, and the military. Major
cities like New York, Chicago, and Boston have
access to tens of thousands of security cameras.
Vy’s unique shape recognition technology can be
programmed to tag and track objects of interest
such as vehicles.
It can also be programmed to create unique
“fingerprints” for a specific vehicle. Other
areas where Vy’s unique technology could prove
useful are searching for rapid acceleration or
deceleration for the purpose of near real-time
Footprint: ellipse object models fitted
to car wheels with Bezier curves in
The ability to reliably represent curves and
objects as mathematical models is critical to
autonomous vehicle steering. Tremendous progress
has been made using laser ranging systems and
millimeter wave radar. We believe that adding
Vy’s shape detection technology to these
capabilities can improve reliability and reduce
cost making inexpensive fully autonomous
personal robots a reality.
Challenge Winner Stanley in 2007
(DARPA Grand Challenge Winner 2013)