Abstract: Our
modern society has pervasive electronic displays such as billboards,
computers,
tablets, signage and kiosks. The prevalence of these displays provides
opportunities to
develop photographic methods for active scenes where intentional
information is encoded
in the display images and must be recovered by a camera. These active
scenes are fundamentally different from traditional passive scenes
because image formation is based
on display emittance, not surface reflectance. QR-codes on billboards
are one example
of an active scene with intentional information, albeit a very simple
case. The problem
becomes more challenging when the message is hidden and dynamic.
Detecting and
decoding the message requires careful photometric modeling for
computational message
recovery. We present a novel method for communicating between a camera
and display
by embedding and recovering information within a displayed image. A
handheld camera
pointed at the display can receive not only the display image, but also
the underlying
message. Unlike standard watermarking and steganography that lie
outside the domain
of computer vision, our message recovery algorithm uses illumination in
order to op-
tically communicate hidden messages in real world scenes. The key
innovation of our
approach is an algorithm to perform simultaneous radiometric
calibration and message
recovery in one convex optimization problem. By modeling the photometry
of the system using a camera-display transfer function (CDTF), we
derive a physics-based kernel
function for support vector machine classification. We demonstrate that
our method of
optimal online radiometric calibration (OORC) leads to an efficient and
robust algorithm
for a computational messaging between various commercial cameras and
displays. An
evaluation of results has been provided by using video messaging with
nine different
combinations of commercial cameras and displays.
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