12,505 research outputs found
Video-rate computational super-resolution and integral imaging at longwave-infrared wavelengths
We report the first computational super-resolved, multi-camera integral
imaging at long-wave infrared (LWIR) wavelengths. A synchronized array of FLIR
Lepton cameras was assembled, and computational super-resolution and
integral-imaging reconstruction employed to generate video with light-field
imaging capabilities, such as 3D imaging and recognition of partially obscured
objects, while also providing a four-fold increase in effective pixel count.
This approach to high-resolution imaging enables a fundamental reduction in the
track length and volume of an imaging system, while also enabling use of
low-cost lens materials.Comment: Supplementary multimedia material in
http://dx.doi.org/10.6084/m9.figshare.530302
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
Depth sensing is useful in a variety of applications that range from
augmented reality to robotics. Time-of-flight (TOF) cameras are appealing
because they obtain dense depth measurements with minimal latency. However, for
many battery-powered devices, the illumination source of a TOF camera is power
hungry and can limit the battery life of the device. To address this issue, we
present an algorithm that lowers the power for depth sensing by reducing the
usage of the TOF camera and estimating depth maps using concurrently collected
images. Our technique also adaptively controls the TOF camera and enables it
when an accurate depth map cannot be estimated. To ensure that the overall
system power for depth sensing is reduced, we design our algorithm to run on a
low power embedded platform, where it outputs 640x480 depth maps at 30 frames
per second. We evaluate our approach on several RGB-D datasets, where it
produces depth maps with an overall mean relative error of 0.96% and reduces
the usage of the TOF camera by 85%. When used with commercial TOF cameras, we
estimate that our algorithm can lower the total power for depth sensing by up
to 73%
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