11,235 research outputs found
Fast, Dense Feature SDM on an iPhone
In this paper, we present our method for enabling dense SDM to run at over 90
FPS on a mobile device. Our contributions are two-fold. Drawing inspiration
from the FFT, we propose a Sparse Compositional Regression (SCR) framework,
which enables a significant speed up over classical dense regressors. Second,
we propose a binary approximation to SIFT features. Binary Approximated SIFT
(BASIFT) features, which are a computationally efficient approximation to SIFT,
a commonly used feature with SDM. We demonstrate the performance of our
algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM
ARKCoS: Artifact-Suppressed Accelerated Radial Kernel Convolution on the Sphere
We describe a hybrid Fourier/direct space convolution algorithm for compact
radial (azimuthally symmetric) kernels on the sphere. For high resolution maps
covering a large fraction of the sky, our implementation takes advantage of the
inexpensive massive parallelism afforded by consumer graphics processing units
(GPUs). Applications involve modeling of instrumental beam shapes in terms of
compact kernels, computation of fine-scale wavelet transformations, and optimal
filtering for the detection of point sources. Our algorithm works for any
pixelization where pixels are grouped into isolatitude rings. Even for kernels
that are not bandwidth limited, ringing features are completely absent on an
ECP grid. We demonstrate that they can be highly suppressed on the popular
HEALPix pixelization, for which we develop a freely available implementation of
the algorithm. As an example application, we show that running on a high-end
consumer graphics card our method speeds up beam convolution for simulations of
a characteristic Planck high frequency instrument channel by two orders of
magnitude compared to the commonly used HEALPix implementation on one CPU core
while maintaining at typical a fractional RMS accuracy of about 1 part in 10^5.Comment: 10 pages, 6 figures. Submitted to Astronomy and Astrophysics.
Replaced to match published version. Code can be downloaded at
https://github.com/elsner/arkco
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