300 research outputs found
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
We propose a unified formulation for the problem of 3D human pose estimation
from a single raw RGB image that reasons jointly about 2D joint estimation and
3D pose reconstruction to improve both tasks. We take an integrated approach
that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN
architecture and uses the knowledge of plausible 3D landmark locations to
refine the search for better 2D locations. The entire process is trained
end-to-end, is extremely efficient and obtains state- of-the-art results on
Human3.6M outperforming previous approaches both on 2D and 3D errors.Comment: Paper presented at CVPR 1
Reduced Memory Region Based Deep Convolutional Neural Network Detection
Accurate pedestrian detection has a primary role in automotive safety: for
example, by issuing warnings to the driver or acting actively on car's brakes,
it helps decreasing the probability of injuries and human fatalities. In order
to achieve very high accuracy, recent pedestrian detectors have been based on
Convolutional Neural Networks (CNN). Unfortunately, such approaches require
vast amounts of computational power and memory, preventing efficient
implementations on embedded systems. This work proposes a CNN-based detector,
adapting a general-purpose convolutional network to the task at hand. By
thoroughly analyzing and optimizing each step of the detection pipeline, we
develop an architecture that outperforms methods based on traditional image
features and achieves an accuracy close to the state-of-the-art while having
low computational complexity. Furthermore, the model is compressed in order to
fit the tight constrains of low power devices with a limited amount of embedded
memory available. This paper makes two main contributions: (1) it proves that a
region based deep neural network can be finely tuned to achieve adequate
accuracy for pedestrian detection (2) it achieves a very low memory usage
without reducing detection accuracy on the Caltech Pedestrian dataset.Comment: IEEE 2016 ICCE-Berli
xR-EgoPose: Egocentric 3D Human Pose from an HMD Camera
We present a new solution to egocentric 3D body pose estimation from
monocular images captured from a downward looking fish-eye camera installed on
the rim of a head mounted virtual reality device. This unusual viewpoint, just
2 cm. away from the user's face, leads to images with unique visual appearance,
characterized by severe self-occlusions and strong perspective distortions that
result in a drastic difference in resolution between lower and upper body. Our
contribution is two-fold. Firstly, we propose a new encoder-decoder
architecture with a novel dual branch decoder designed specifically to account
for the varying uncertainty in the 2D joint locations. Our quantitative
evaluation, both on synthetic and real-world datasets, shows that our strategy
leads to substantial improvements in accuracy over state of the art egocentric
pose estimation approaches. Our second contribution is a new large-scale
photorealistic synthetic dataset - xR-EgoPose - offering 383K frames of high
quality renderings of people with a diversity of skin tones, body shapes,
clothing, in a variety of backgrounds and lighting conditions, performing a
range of actions. Our experiments show that the high variability in our new
synthetic training corpus leads to good generalization to real world footage
and to state of the art results on real world datasets with ground truth.
Moreover, an evaluation on the Human3.6M benchmark shows that the performance
of our method is on par with top performing approaches on the more classic
problem of 3D human pose from a third person viewpoint.Comment: ICCV 201
Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture
We propose a CNN-based approach for multi-camera markerless motion capture of
the human body. Unlike existing methods that first perform pose estimation on
individual cameras and generate 3D models as post-processing, our approach
makes use of 3D reasoning throughout a multi-stage approach. This novelty
allows us to use provisional 3D models of human pose to rethink where the
joints should be located in the image and to recover from past mistakes. Our
principled refinement of 3D human poses lets us make use of image cues, even
from images where we previously misdetected joints, to refine our estimates as
part of an end-to-end approach. Finally, we demonstrate how the high-quality
output of our multi-camera setup can be used as an additional training source
to improve the accuracy of existing single camera models.Comment: International Conference on 3DVision (3dv
Clinical outcomes of guided tissue regeneration procedure utilized with two different surgical approaches - a comparative study
The guided tissue regeneration (Nyman et al. 1982) is a well-established surgical technique which main goal is to reconstruct the periodontal ligament with functional collagen fibers inserted into a newly formed cementum and alveolar bone. Teeth with periodontal disease resulting in deep infrabony pockets are successfully treated with this technique. Its main prognostic factors from clinical and biological standpoint include: blood clot stabilization, primary closure of the defect, space provision and exclusion from the gingival tissues. Several surgical techniques have been proposed for utilization of GTR. Lately these techniques have been aiming at minimal invasiveness for optimal wound closure and lesser postoperative morbidity. The aim of this presentation was to compare the clinical outcomes of two different techniques for GTR:modified papilla preservation flap (Cortelinni et al, 1995) vs. single flap approach.
Results: The obtained data revealed significantly better results in CAL gain (3.6+/-1.3 mm vs. 2.1+/- 1.2 ), PD reduction (2.7+/- 0.8 vs. 1.4 +/-0.6) and REC ( 1.5 +/- 0.9 vs. 2.6 +/- 0.8) at baseline and one year post surgery in test group.
Conclusion: Results from our analysis suggest that single flap approach as less invasive provides better clinical outcomes, although without big clinical relevance considering the small number of patients
Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions
We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe
Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC
Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe
Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV
Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe
Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV
Peer reviewe
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