
After
more than 50 years of research, machine
vision technology has recently reached the point in its development where it is
ready for commercial application. Now, the rush is on to make the most
profitable use of it.
Machine
vision technology teams up specialized computer hardware, cameras, and software
to create a machine that can see and recognize objects and people and then make
useful interpretations of those images. One application described recently by CNet
News,1 is a system from Hyperactive
Technologies that monitors operations at fast food restaurants. That machine
vision system keeps track of cars coming into the parking lot and then compares
the anticipated demand to the current amount of cooked food that is available.
By calculating cooking times, it can alert the staff ahead of time as to what
food items they will need to prepare next, thereby improving efficiency, as
well as customer satisfaction. The system is currently being tested at
Popeye’s Chicken and Jack in the Box outlets. Zaxby’s, which already has the
system installed at more than 100 restaurants in its chain, says it is saving
over $800,000 a year because the system wastes less food.
According
to The Economist Technology Quarterly,2 these types of systems are being used to
improve efficiency in the manufacture of everyday goods, such as diapers. If a
more accurate machine vision system can reduce the wasted material that is cut
by even just one millimeter per diaper, it can save millions of dollars for a
company that produces billions of diapers.
Another
example involves companies that sell consumer packaged foods, such as rice.
Rice sorters are using computer vision to scan four tons of rice an hour and
then use air jets to eject discolored grains, rocks, or other debris. That
requires the extremely fast and accurate computer vision systems that are now
becoming widely available.
Omron
Corporation in Japan is developing a system that can tell if employees, such as
cashiers, are smiling at customers or not. The same sort of system can be used
to determine if an expression is authentic or not. Unilever is using it to
determine how food tasters are reacting to a product. Procter & Gamble is
also using it to...