44,767 research outputs found
Non-rigid Reconstruction with a Single Moving RGB-D Camera
We present a novel non-rigid reconstruction method using a moving RGB-D
camera. Current approaches use only non-rigid part of the scene and completely
ignore the rigid background. Non-rigid parts often lack sufficient geometric
and photometric information for tracking large frame-to-frame motion. Our
approach uses camera pose estimated from the rigid background for foreground
tracking. This enables robust foreground tracking in situations where large
frame-to-frame motion occurs. Moreover, we are proposing a multi-scale
deformation graph which improves non-rigid tracking without compromising the
quality of the reconstruction. We are also contributing a synthetic dataset
which is made publically available for evaluating non-rigid reconstruction
methods. The dataset provides frame-by-frame ground truth geometry of the
scene, the camera trajectory, and masks for background foreground. Experimental
results show that our approach is more robust in handling larger frame-to-frame
motions and provides better reconstruction compared to state-of-the-art
approaches.Comment: Accepted in International Conference on Pattern Recognition (ICPR
2018
A new analysis strategy for detection of faint gamma-ray sources with Imaging Atmospheric Cherenkov Telescopes
A new background rejection strategy for gamma-ray astrophysics with
stereoscopic Imaging Atmospheric Cherenkov Telescopes (IACT), based on Monte
Carlo (MC) simulations and real background data from the H.E.S.S. [High Energy
Stereoscopic System, see [1].] experiment, is described. The analysis is based
on a multivariate combination of both previously-known and newly-derived
discriminant variables using the physical shower properties, as well as its
multiple images, for a total of eight variables. Two of these new variables are
defined thanks to a new energy evaluation procedure, which is also presented
here. The method allows an enhanced sensitivity with the current generation of
ground-based Cherenkov telescopes to be achieved, and at the same time its main
features of rapidity and flexibility allow an easy generalization to any type
of IACT. The robustness against Night Sky Background (NSB) variations of this
approach is tested with MC simulated events. The overall consistency of the
analysis chain has been checked by comparison of the real gamma-ray signal
obtained from H.E.S.S. observations with MC simulations and through
reconstruction of known source spectra. Finally, the performance has been
evaluated by application to faint H.E.S.S. sources. The gain in sensitivity as
compared to the best standard Hillas analysis ranges approximately from 1.2 to
1.8 depending on the source characteristics, which corresponds to an economy in
observation time of a factor 1.4 to 3.2.Comment: 26 pages, 13 figure
MonoPerfCap: Human Performance Capture from Monocular Video
We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.Comment: Accepted to ACM TOG 2018, to be presented on SIGGRAPH 201
ArborZ: Photometric Redshifts Using Boosted Decision Trees
Precision photometric redshifts will be essential for extracting cosmological
parameters from the next generation of wide-area imaging surveys. In this paper
we introduce a photometric redshift algorithm, ArborZ, based on the
machine-learning technique of Boosted Decision Trees. We study the algorithm
using galaxies from the Sloan Digital Sky Survey and from mock catalogs
intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show
that it improves upon the performance of existing algorithms. Moreover, the
method naturally leads to the reconstruction of a full probability density
function (PDF) for the photometric redshift of each galaxy, not merely a single
"best estimate" and error, and also provides a photo-z quality figure-of-merit
for each galaxy that can be used to reject outliers. We show that the stacked
PDFs yield a more accurate reconstruction of the redshift distribution N(z). We
discuss limitations of the current algorithm and ideas for future work.Comment: 10 pages, 13 figures, submitted to Ap
Supersampling and network reconstruction of urban mobility
Understanding human mobility is of vital importance for urban planning,
epidemiology, and many other fields that aim to draw policies from the
activities of humans in space. Despite recent availability of large scale data
sets related to human mobility such as GPS traces, mobile phone data, etc., it
is still true that such data sets represent a subsample of the population of
interest, and then might give an incomplete picture of the entire population in
question. Notwithstanding the abundant usage of such inherently limited data
sets, the impact of sampling biases on mobility patterns is unclear -- we do
not have methods available to reliably infer mobility information from a
limited data set. Here, we investigate the effects of sampling using a data set
of millions of taxi movements in New York City. On the one hand, we show that
mobility patterns are highly stable once an appropriate simple rescaling is
applied to the data, implying negligible loss of information due to subsampling
over long time scales. On the other hand, contrasting an appropriate null model
on the weighted network of vehicle flows reveals distinctive features which
need to be accounted for. Accordingly, we formulate a "supersampling"
methodology which allows us to reliably extrapolate mobility data from a
reduced sample and propose a number of network-based metrics to reliably assess
its quality (and that of other human mobility models). Our approach provides a
well founded way to exploit temporal patterns to save effort in recording
mobility data, and opens the possibility to scale up data from limited records
when information on the full system is needed.Comment: 14 pages, 4 figure
Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation
Accounting for 26% of all new cancer cases worldwide, breast cancer remains
the most common form of cancer in women. Although early breast cancer has a
favourable long-term prognosis, roughly a third of patients suffer from a
suboptimal aesthetic outcome despite breast conserving cancer treatment.
Clinical-quality 3D modelling of the breast surface therefore assumes an
increasingly important role in advancing treatment planning, prediction and
evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive
and either infrastructure-heavy or subject to motion artefacts. In this paper
we employ a single consumer-grade RGBD camera with an ICP-based registration
approach to jointly align all points from a sequence of depth images
non-rigidly. Subtle body deformation due to postural sway and respiration is
successfully mitigated leading to a higher geometric accuracy through
regularised locally affine transformations. We present results from 6 clinical
cases where our method compares well with the gold standard and outperforms a
previous approach. We show that our method produces better reconstructions
qualitatively by visual assessment and quantitatively by consistently obtaining
lower landmark error scores and yielding more accurate breast volume estimates
Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly available.
Parallel to this is the need for new methods of reconstructing (inferring) the
topology of networks from time-resolved observations of their node-dynamics.
The methods based on physical insights often rely on strong assumptions about
the properties and dynamics of the scrutinized network. Here, we use the
insights from machine learning to design a new method of network reconstruction
that essentially makes no such assumptions. Specifically, we interpret the
available trajectories (data) as features, and use two independent feature
ranking approaches -- Random forest and RReliefF -- to rank the importance of
each node for predicting the value of each other node, which yields the
reconstructed adjacency matrix. We show that our method is fairly robust to
coupling strength, system size, trajectory length and noise. We also find that
the reconstruction quality strongly depends on the dynamical regime
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
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