22,351 research outputs found
Intrinsic Dynamic Shape Prior for Fast, Sequential and Dense Non-Rigid Structure from Motion with Detection of Temporally-Disjoint Rigidity
While dense non-rigid structure from motion (NRSfM) has been extensively studied from the perspective of the reconstructability problem over the recent years, almost no attempts have been undertaken to bring it into the practical realm. The reasons for the slow dissemination are the severe ill-posedness, high sensitivity to motion and deformation cues and the difficulty to obtain reliable point tracks in the vast majority of practical scenarios. To fill this gap, we propose a hybrid approach that extracts prior shape knowledge from an input sequence with NRSfM and uses it as a dynamic shape prior for sequential surface recovery in scenarios with recurrence. Our Dynamic Shape Prior Reconstruction (DSPR) method can be combined with existing dense NRSfM techniques while its energy functional is optimised with stochastic gradient descent at real-time rates for new incoming point tracks. The proposed versatile framework with a new core NRSfM approach outperforms several other methods in the ability to handle inaccurate and noisy point tracks, provided we have access to a representative (in terms of the deformation variety) image sequence. Comprehensive experiments highlight convergence properties and the accuracy of DSPR under different disturbing effects. We also perform a joint study of tracking and reconstruction and show applications to shape compression and heart reconstruction under occlusions. We achieve state-of-the-art metrics (accuracy and compression ratios) in different scenarios
Measuring cellular traction forces on non-planar substrates
Animal cells use traction forces to sense the mechanics and geometry of their
environment. Measuring these traction forces requires a workflow combining cell
experiments, image processing and force reconstruction based on elasticity
theory. Such procedures have been established before mainly for planar
substrates, in which case one can use the Green's function formalism. Here we
introduce a worksflow to measure traction forces of cardiac myofibroblasts on
non-planar elastic substrates. Soft elastic substrates with a wave-like
topology were micromolded from polydimethylsiloxane (PDMS) and fluorescent
marker beads were distributed homogeneously in the substrate. Using feature
vector based tracking of these marker beads, we first constructed a hexahedral
mesh for the substrate. We then solved the direct elastic boundary volume
problem on this mesh using the finite element method (FEM). Using data
simulations, we show that the traction forces can be reconstructed from the
substrate deformations by solving the corresponding inverse problem with a
L1-norm for the residue and a L2-norm for 0th order Tikhonov regularization.
Applying this procedure to the experimental data, we find that cardiac
myofibroblast cells tend to align both their shapes and their forces with the
long axis of the deformable wavy substrate.Comment: 34 pages, 9 figure
Using data visualization to deduce faces expressions
Conferência Internacional, realizada na Turquia, de 6-8 de setembro de 2018.Collect and examine in real time multi modal sensor data of a human face, is an important problem in computer vision, with applications in medical and monitoring analysis, entertainment and security. Although its advances, there are still many open issues in terms of the identification of the facial expression. Different algorithms and approaches have been developed to find out patterns and characteristics that can help the automatic expression identification. One way to study data is through data visualizations. Data visualization turns numbers and letters into aesthetically pleasing visuals, making it easy to recognize patterns and find exceptions. In this article, we use information visualization as a tool to analyse data points and find out possible existing patterns in four different facial expressions.info:eu-repo/semantics/publishedVersio
3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy
Recently we have developed an algorithm for reconstructing volumetric images
and extracting 3D tumor motion information from a single x-ray projection. We
have demonstrated its feasibility using a digital respiratory phantom with
regular breathing patterns. In this work, we present a detailed description and
a comprehensive evaluation of the improved algorithm. The algorithm was
improved by incorporating respiratory motion prediction. The accuracy and
efficiency were then evaluated on 1) a digital respiratory phantom, 2) a
physical respiratory phantom, and 3) five lung cancer patients. These
evaluation cases include both regular and irregular breathing patterns that are
different from the training dataset. For the digital respiratory phantom with
regular and irregular breathing, the average 3D tumor localization error is
less than 1 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time
for 3D tumor localization from each projection ranges between 0.19 and 0.26
seconds, for both regular and irregular breathing, which is about a 10%
improvement over previously reported results. For the physical respiratory
phantom, an average tumor localization error below 1 mm was achieved with an
average computation time of 0.13 and 0.16 seconds on the same GPU card, for
regular and irregular breathing, respectively. For the five lung cancer
patients, the average tumor localization error is below 2 mm in both the axial
and tangential directions. The average computation time on the same GPU card
ranges between 0.26 and 0.34 seconds
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