1,095 research outputs found
Microdensitometer scanning of SL3 S190 imagery and accompanying step wedges
There are no author-identified significant results in this report
Pluto's global surface composition through pixel-by-pixel Hapke modeling of New Horizons Ralph/LEISA data
On July 14th 2015, NASA's New Horizons mission gave us an unprecedented
detailed view of the Pluto system. The complex compositional diversity of
Pluto's encounter hemisphere was revealed by the Ralph/LEISA infrared
spectrometer on board of New Horizons. We present compositional maps of Pluto
defining the spatial distribution of the abundance and textural properties of
the volatiles methane and nitrogen ices and non-volatiles water ice and tholin.
These results are obtained by applying a pixel-by-pixel Hapke radiative
transfer model to the LEISA scans. Our analysis focuses mainly on the large
scale latitudinal variations of methane and nitrogen ices and aims at setting
observational constraints to volatile transport models. Specifically, we find
three latitudinal bands: the first, enriched in methane, extends from the pole
to 55deg N, the second dominated by nitrogen, continues south to 35deg N, and
the third, composed again mainly of methane, reaches 20deg N. We demonstrate
that the distribution of volatiles across these surface units can be explained
by differences in insolation over the past few decades. The latitudinal pattern
is broken by Sputnik Planitia, a large reservoir of volatiles, with nitrogen
playing the most important role. The physical properties of methane and
nitrogen in this region are suggestive of the presence of a cold trap or
possible volatile stratification. Furthermore our modeling results point to a
possible sublimation transport of nitrogen from the northwest edge of Sputnik
Planitia toward the south.Comment: 43 pages, 7 figures; accepted for publication in Icaru
Variational Uncalibrated Photometric Stereo under General Lighting
Photometric stereo (PS) techniques nowadays remain constrained to an ideal
laboratory setup where modeling and calibration of lighting is amenable. To
eliminate such restrictions, we propose an efficient principled variational
approach to uncalibrated PS under general illumination. To this end, the
Lambertian reflectance model is approximated through a spherical harmonic
expansion, which preserves the spatial invariance of the lighting. The joint
recovery of shape, reflectance and illumination is then formulated as a single
variational problem. There the shape estimation is carried out directly in
terms of the underlying perspective depth map, thus implicitly ensuring
integrability and bypassing the need for a subsequent normal integration. To
tackle the resulting nonconvex problem numerically, we undertake a two-phase
procedure to initialize a balloon-like perspective depth map, followed by a
"lagged" block coordinate descent scheme. The experiments validate efficiency
and robustness of this approach. Across a variety of evaluations, we are able
to reduce the mean angular error consistently by a factor of 2-3 compared to
the state-of-the-art.Comment: Haefner and Ye contributed equall
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
As many different 3D volumes could produce the same 2D x-ray image, inverting
this process is challenging. We show that recent deep learning-based
convolutional neural networks can solve this task. As the main challenge in
learning is the sheer amount of data created when extending the 2D image into a
3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which
is then fused in a second step with the input x-ray into a high-resolution
volume. To train and validate our approach we introduce a new dataset that
comprises of close to half a million computer-simulated 2D x-ray images of 3D
volumes scanned from 175 mammalian species. Applications of our approach
include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays
including changes of illumination, view pose or geometry. Our evaluation
includes comparison to previous tomography work, previous learning methods
using our data, a user study and application to a set of real x-rays
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