2,814 research outputs found
Methods for Volumetric Reconstruction of Visual Scenes
In this paper, we present methods for 3D volumetric reconstruction of visual scenes photographed by multiple calibrated cameras placed at arbitrary viewpoints. Our goal is to generate a 3D model that can be rendered to synthesize new photo-realistic views of the scene. We improve upon existing voxel coloring/space carving approaches by introducing new ways to compute visibility and photo-consistency, as well as model infinitely large scenes. In particular, we describe a visibility approach that uses all possible color information from the photographs during reconstruction, photo-consistency measures that are more robust and/or require less manual intervention, and a volumetric warping method for application of these reconstruction methods to large-scale scenes
Graphene formation on SiC substrates
Graphene layers were created on both C and Si faces of semi-insulating,
on-axis, 4H- and 6H-SiC substrates. The process was performed under high vacuum
(<10-4 mbar) in a commercial chemical vapor deposition SiC reactor. A method
for H2 etching the on-axis sub-strates was developed to produce surface steps
with heights of 0.5 nm on the Si-face and 1.0 to 1.5 nm on the C-face for each
polytype. A process was developed to form graphene on the substrates
immediately after H2 etching and Raman spectroscopy of these samples confirmed
the formation of graphene. The morphology of the graphene is described. For
both faces, the underlying substrate morphology was significantly modified
during graphene formation; sur-face steps were up to 15 nm high and the uniform
step morphology was sometimes lost. Mo-bilities and sheet carrier
concentrations derived from Hall Effect measurements on large area (16 mm
square) and small area (2 and 10 um square) samples are presented and shown to
compare favorably to recent reports.Comment: European Conference on Silicon Carbide and Related Materials 2008
(ECSCRM '08), 4 pages, 4 figure
A categorical foundation for Bayesian probability
Given two measurable spaces and with countably generated
-algebras, a perfect prior probability measure on and a
sampling distribution , there is a corresponding inference
map which is unique up to a set of measure zero. Thus,
given a data measurement , a posterior probability
can be computed. This procedure is iterative: with
each updated probability , we obtain a new joint distribution which in
turn yields a new inference map and the process repeats with each
additional measurement. The main result uses an existence theorem for regular
conditional probabilities by Faden, which holds in more generality than the
setting of Polish spaces. This less stringent setting then allows for
non-trivial decision rules (Eilenberg--Moore algebras) on finite (as well as
non finite) spaces, and also provides for a common framework for decision
theory and Bayesian probability.Comment: 15 pages; revised setting to more clearly explain how to incorporate
perfect measures and the Giry monad; to appear in Applied Categorical
Structure
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