10,595 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach
Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft
Automatic aerial target detection and tracking system in airborne FLIR images based on efficient target trajectory filtering
Common strategies for detection and tracking of aerial moving targets in airborne Forward-Looking Infrared
(FLIR) images offer accurate results in images composed by a non-textured sky. However, when cloud and
earth regions appear in the image sequence, those strategies result in an over-detection that increases very
significantly the false alarm rate. Besides, the airborne camera induces a global motion in the image sequence
that complicates even more detection and tracking tasks. In this work, an automatic detection and tracking
system with an innovative and efficient target trajectory filtering is presented. It robustly compensates the
global motion to accurately detect and track potential aerial targets. Their trajectories are analyzed by a curve
fitting technique to reliably validate real targets. This strategy allows to filter false targets with stationary or
erratic trajectories. The proposed system makes special emphasis in the use of low complexity video analysis
techniques to achieve real-time operation. Experimental results using real FLIR sequences show a dramatic
reduction of the false alarm rate, while maintaining the detection rate
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
The interstellar cloud surrounding the Sun: a new perspective
Aims: We offer a new, simpler picture of the local interstellar medium, made
of a single continuous cloud enveloping the Sun. This new outlook enables the
description of a diffuse cloud from within and brings to light some unexpected
properties. Methods: We re-examine the kinematics and abundances of the local
interstellar gas, as revealed by the published results for the ultraviolet
absorption lines of MgII, FeII, and HI. Results: In contrast to previous
representations, our new picture of the local interstellar medium consists of a
single, monolithic cloud that surrounds the Sun in all directions and accounts
for most of the matter present in the first 50 parsecs around the Sun. The
cloud fills the space around us out to about 9 pc in most directions, although
its boundary is very irregular with possibly a few extensions up to 20 pc. The
cloud does not behave like a rigid body: gas within the cloud is being
differentially decelerated in the direction of motion, and the cloud is
expanding in directions perpendicular to this flow, much like a squashed
balloon. Average HI volume densities inside the cloud vary between 0.03 and 0.1
cm-3 over different directions. Metals appear to be significantly depleted onto
grains, and there is a steady increase in depletion from the rear of the cloud
to the apex of motion. There is no evidence that changes in the ionizing
radiation influence the apparent abundances. Secondary absorption components
are detected in 60% of the sight lines. Almost all of them appear to be
interior to the volume occupied by the main cloud. Half of the sight lines
exhibit a secondary component moving at about -7.2 km/s with respect to the
main component, which may be the signature of a shock propagating toward the
cloud's interior.Comment: Accepted for publication in Astronomy & Astrophysic
Methodology for tidal turbine representation in ocean circulation model
The present method proposes the use and adaptation of ocean circulation models as an assessment tool framework for tidal current turbine (TCT) array layout optimization. By adapting both momentum and turbulence transport equations of an existing model, the present TCT representation method is proposed to extend the actuator disc concept to 3-D large-scale ocean circulation models. Through the reproduction of experimental flume tests and grid dependency tests, this method has shown its numerical coherence as well as its ability to simulate accurately both momentum and turbulent turbine-induced perturbations in both near and far wakes in a relatively short period of computation time. Consequently the present TCT representation method is a very promising basis for the development of a TCT array layout optimization tool
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