54,502 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
Non-convex optimization for 3D point source localization using a rotating point spread function
We consider the high-resolution imaging problem of 3D point source image
recovery from 2D data using a method based on point spread function (PSF)
engineering. The method involves a new technique, recently proposed by
S.~Prasad, based on the use of a rotating PSF with a single lobe to obtain
depth from defocus. The amount of rotation of the PSF encodes the depth
position of the point source. Applications include high-resolution single
molecule localization microscopy as well as the problem addressed in this paper
on localization of space debris using a space-based telescope. The localization
problem is discretized on a cubical lattice where the coordinates of nonzero
entries represent the 3D locations and the values of these entries the fluxes
of the point sources. Finding the locations and fluxes of the point sources is
a large-scale sparse 3D inverse problem. A new nonconvex regularization method
with a data-fitting term based on Kullback-Leibler (KL) divergence is proposed
for 3D localization for the Poisson noise model. In addition, we propose a new
scheme of estimation of the source fluxes from the KL data-fitting term.
Numerical experiments illustrate the efficiency and stability of the algorithms
that are trained on a random subset of image data before being applied to other
images. Our 3D localization algorithms can be readily applied to other kinds of
depth-encoding PSFs as well.Comment: 28 page
Extended depth-of-field imaging and ranging in a snapshot
Traditional approaches to imaging require that an increase in depth of field is associated with a reduction in
numerical aperture, and hence with a reduction in resolution and optical throughput. In their seminal
work, Dowski and Cathey reported how the asymmetric point-spread function generated by a cubic-phase
aberration encodes the detected image such that digital recovery can yield images with an extended depth of
field without sacrificing resolution [Appl. Opt. 34, 1859 (1995)]. Unfortunately recovered images are
generally visibly degraded by artifacts arising from subtle variations in point-spread functions with defocus.
We report a technique that involves determination of the spatially variant translation of image components
that accompanies defocus to enable determination of spatially variant defocus. This in turn enables recovery
of artifact-free, extended depth-of-field images together with a two-dimensional defocus and range map
of the imaged scene. We demonstrate the technique for high-quality macroscopic and microscopic imaging
of scenes presenting an extended defocus of up to two waves, and for generation of defocus maps with an
uncertainty of 0.036 waves
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
Designing electronic properties of two-dimensional crystals through optimization of deformations
One of the enticing features common to most of the two-dimensional electronic
systems that are currently at the forefront of materials science research is
the ability to easily introduce a combination of planar deformations and
bending in the system. Since the electronic properties are ultimately
determined by the details of atomic orbital overlap, such mechanical
manipulations translate into modified electronic properties. Here, we present a
general-purpose optimization framework for tailoring physical properties of
two-dimensional electronic systems by manipulating the state of local strain,
allowing a one-step route from their design to experimental implementation. A
definite example, chosen for its relevance in light of current experiments in
graphene nanostructures, is the optimization of the experimental parameters
that generate a prescribed spatial profile of pseudomagnetic fields in
graphene. But the method is general enough to accommodate a multitude of
possible experimental parameters and conditions whereby deformations can be
imparted to the graphene lattice, and complies, by design, with graphene's
elastic equilibrium and elastic compatibility constraints. As a result, it
efficiently answers the inverse problem of determining the optimal values of a
set of external or control parameters that result in a graphene deformation
whose associated pseudomagnetic field profile best matches a prescribed target.
The ability to address this inverse problem in an expedited way is one key step
for practical implementations of the concept of two-dimensional systems with
electronic properties strain-engineered to order. The general-purpose nature of
this calculation strategy means that it can be easily applied to the
optimization of other relevant physical quantities which directly depend on the
local strain field, not just in graphene but in other two-dimensional
electronic membranes.Comment: 37 pages, 9 figures. This submission contains low-resolution bitmap
images; high-resolution images can be found in version 1, which is ~13.5 M
Autocalibration with the Minimum Number of Cameras with Known Pixel Shape
In 3D reconstruction, the recovery of the calibration parameters of the
cameras is paramount since it provides metric information about the observed
scene, e.g., measures of angles and ratios of distances. Autocalibration
enables the estimation of the camera parameters without using a calibration
device, but by enforcing simple constraints on the camera parameters. In the
absence of information about the internal camera parameters such as the focal
length and the principal point, the knowledge of the camera pixel shape is
usually the only available constraint. Given a projective reconstruction of a
rigid scene, we address the problem of the autocalibration of a minimal set of
cameras with known pixel shape and otherwise arbitrarily varying intrinsic and
extrinsic parameters. We propose an algorithm that only requires 5 cameras (the
theoretical minimum), thus halving the number of cameras required by previous
algorithms based on the same constraint. To this purpose, we introduce as our
basic geometric tool the six-line conic variety (SLCV), consisting in the set
of planes intersecting six given lines of 3D space in points of a conic. We
show that the set of solutions of the Euclidean upgrading problem for three
cameras with known pixel shape can be parameterized in a computationally
efficient way. This parameterization is then used to solve autocalibration from
five or more cameras, reducing the three-dimensional search space to a
two-dimensional one. We provide experiments with real images showing the good
performance of the technique.Comment: 19 pages, 14 figures, 7 tables, J. Math. Imaging Vi
EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments
We review developments, issues and challenges in Electrical Impedance
Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT,
Manchester 2003. We focus on the necessity for three dimensional data
collection and reconstruction, efficient solution of the forward problem and
present and future reconstruction algorithms. We also suggest common pitfalls
or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of
EIT, Manchester, UK, 200
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
- …