1,271 research outputs found
Tomographic inversion using -norm regularization of wavelet coefficients
We propose the use of regularization in a wavelet basis for the
solution of linearized seismic tomography problems , allowing for the
possibility of sharp discontinuities superimposed on a smoothly varying
background. An iterative method is used to find a sparse solution that
contains no more fine-scale structure than is necessary to fit the data to
within its assigned errors.Comment: 19 pages, 14 figures. Submitted to GJI July 2006. This preprint does
not use GJI style files (which gives wrong received/accepted dates).
Corrected typ
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regularize the ICP registration technique. We also present a
technique to filter the pairs of 3D matched points based on the distribution of
their distances. This filter is implemented efficiently on the GPU. Estimating
the distribution of the distances helps control the number of iterations
necessary for the convergence of the ICP algorithm. Finally, we show
experimental results that highlight improvements in robustness, a speed-up of
almost 12%, and a gain in tracking quality of 53% for the ATE metric on the
Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications
Based on Location Clues 201
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
An Improved Observation Model for Super-Resolution under Affine Motion
Super-resolution (SR) techniques make use of subpixel shifts between frames
in an image sequence to yield higher-resolution images. We propose an original
observation model devoted to the case of non isometric inter-frame motion as
required, for instance, in the context of airborne imaging sensors. First, we
describe how the main observation models used in the SR literature deal with
motion, and we explain why they are not suited for non isometric motion. Then,
we propose an extension of the observation model by Elad and Feuer adapted to
affine motion. This model is based on a decomposition of affine transforms into
successive shear transforms, each one efficiently implemented by row-by-row or
column-by-column 1-D affine transforms.
We demonstrate on synthetic and real sequences that our observation model
incorporated in a SR reconstruction technique leads to better results in the
case of variable scale motions and it provides equivalent results in the case
of isometric motions
CHARA/MIRC observations of two M supergiants in Perseus OB1: temperature, Bayesian modeling, and compressed sensing imaging
Two red supergiants of the Per OB1 association, RS Per and T Per, have been
observed in H band using the MIRC instrument at the CHARA array. The data show
clear evidence of departure from circular symmetry. We present here new
techniques specially developed to analyze such cases, based on state-of-the-art
statistical frameworks. The stellar surfaces are first modeled as limb-darkened
discs based on SATLAS models that fit both MIRC interferometric data and
publicly available spectrophotometric data. Bayesian model selection is then
used to determine the most probable number of spots. The effective surface
temperatures are also determined and give further support to the recently
derived hotter temperature scales of red su- pergiants. The stellar surfaces
are reconstructed by our model-independent imaging code SQUEEZE, making use of
its novel regularizer based on Compressed Sensing theory. We find excellent
agreement between the model-selection results and the reconstructions. Our
results provide evidence for the presence of near-infrared spots representing
about 3-5% of the stellar flux
TomograPy: A Fast, Instrument-Independent, Solar Tomography Software
Solar tomography has progressed rapidly in recent years thanks to the
development of robust algorithms and the availability of more powerful
computers. It can today provide crucial insights in solving issues related to
the line-of-sight integration present in the data of solar imagers and
coronagraphs. However, there remain challenges such as the increase of the
available volume of data, the handling of the temporal evolution of the
observed structures, and the heterogeneity of the data in multi-spacecraft
studies.
We present a generic software package that can perform fast tomographic
inversions that scales linearly with the number of measurements, linearly with
the length of the reconstruction cube (and not the number of voxels) and
linearly with the number of cores and can use data from different sources and
with a variety of physical models: TomograPy
(http://nbarbey.github.com/TomograPy/), an open-source software freely
available on the Python Package Index. For performance, TomograPy uses a
parallelized-projection algorithm. It relies on the World Coordinate System
standard to manage various data sources. A variety of inversion algorithms are
provided to perform the tomographic-map estimation. A test suite is provided
along with the code to ensure software quality. Since it makes use of the
Siddon algorithm it is restricted to rectangular parallelepiped voxels but the
spherical geometry of the corona can be handled through proper use of priors.
We describe the main features of the code and show three practical examples
of multi-spacecraft tomographic inversions using STEREO/EUVI and STEREO/COR1
data. Static and smoothly varying temporal evolution models are presented.Comment: 21 pages, 6 figures, 5 table
Optimization Methods for Inverse Problems
Optimization plays an important role in solving many inverse problems.
Indeed, the task of inversion often either involves or is fully cast as a
solution of an optimization problem. In this light, the mere non-linear,
non-convex, and large-scale nature of many of these inversions gives rise to
some very challenging optimization problems. The inverse problem community has
long been developing various techniques for solving such optimization tasks.
However, other, seemingly disjoint communities, such as that of machine
learning, have developed, almost in parallel, interesting alternative methods
which might have stayed under the radar of the inverse problem community. In
this survey, we aim to change that. In doing so, we first discuss current
state-of-the-art optimization methods widely used in inverse problems. We then
survey recent related advances in addressing similar challenges in problems
faced by the machine learning community, and discuss their potential advantages
for solving inverse problems. By highlighting the similarities among the
optimization challenges faced by the inverse problem and the machine learning
communities, we hope that this survey can serve as a bridge in bringing
together these two communities and encourage cross fertilization of ideas.Comment: 13 page
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
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