DOI: 10.1145/3142361 COP YRIGHT HELD BY AUTHORS
wrong; factors such as the
lighting conditions affect
the quality of the results.
We put a lot of design
work, and many iterations
of our prototype into
getting the user interaction
right. This seems a
fruitful area for further
work—helping people to
understand the limitations
of the AI algorithms
that they use and how to
provide the best level of
feedback to steer those
algorithms to success.
Daniel Harrison, University
College London and Microsoft
Research Cambridge
Richard Banks, Microsoft
Research Cambridge
Tim Regan, Microsoft
Research Cambridge
Martin Grayson, Microsoft
Research Cambridge
→
thingameteam@microsoft.com
http://www.thinga.me/
@thingameapp
Describe what you made.
Collecting has been
practiced for hundreds, if
not thousands, of years.
Collections of purely digital
content such as MP3s,
Kindle books, and even
items collected in computer
games are now common,
as are digitized museum
and gallery collections.
However, little is known
about how people might
react to, and interact with,
personal digital collections
of actual physical objects.
To explore this area of
digital-physical collecting,
we developed Thinga.Me,
an app that allows users to
digitally “collect” the things
they see in the real world.
With Thinga.Me, users take
photographs of their items,
which they can easily cut
out and arrange in stylized
digital collections. These can
then be shared with others.
By removing items from the
background, the app gives
them depth, creating the
illusion of a physical object
and giving users the sense
of carrying their items with
them in their pocket.
What for you is the most
important/interesting thing
about what you made?
Using Thinga. Me as a
research vehicle, we were
able to compare and contrast
people’s sense of their
physical collections with a
literal, digital equivalent.
People were inspired by the
app and put a lot of effort in,
surprise for us was the range
of curious collections people
created and the huge amount
of effort they put in.
Was there anything new for
you in the making process,
materials, or something else
that you can tell us about?
The algorithm we used to
segment the “thing” from
the background, GrabCut,
is relatively old technology,
first described in ACM
Transactions on Graphics
in 2004 and the basis of
PowerPoint’s background-removal tool in Office
2010. However, it was clear
to us that it could form the
centerpiece of our app: The
algorithm provides the
magic that pops the thing
from its background. We
learned that old algorithms
can still appear novel
and magical if properly
showcased in an app.
GrabCut sometimes gets
the initial segmentation
→ Football helmets collected by
one of our participants.
Thinga.Me
Specs
Materials: N/A
Tools: C#, Swift,
tSQL, ASP .Net Core,
the Dapper micro-ORM, Visual Studio,
Xcode, IIS, SQL
Server, Azure
particularly with displaying
their physical collectables.
Did anything go wrong?
The first version of
the application relied
on computer-vision
technology to recognize
and appropriately tag
the items in pictures.
Unfortunately, this system
didn’t work as expected
and often made interesting
and unforeseen suggestions,
such as proposing to tag a
scrunched-up ball of paper
as “hermit crab.”
What was the biggest surprise in making this?
People were really engaged!
We invited 800 people to
test the app and within three
months they had collectively
amassed over 5,500 items in
more than 650 collections—
an average of almost seven
items per user. One user
alone collected over 600
items in the app. The biggest
→ Some initial wireframes of the app.
INTERACTIONS.ACM.ORG 14 INTERACTIONS NOVEMBER–DECEMBER2017
HOW
WAS I T
MADE?
