113,809 research outputs found
Toward optimal X-ray flux utilization in breast CT
A realistic computer-simulation of a breast computed tomography (CT) system
and subject is constructed. The model is used to investigate the optimal number
of views for the scan given a fixed total X-ray fluence. The reconstruction
algorithm is based on accurate solution to a constrained, TV-minimization
problem, which has received much interest recently for sparse-view CT data.Comment: accepted to the 11th International Meeting on Fully Three-Dimensional
Image Reconstruction in Radiology and Nuclear Medicine 201
View Selection with Geometric Uncertainty Modeling
Estimating positions of world points from features observed in images is a
key problem in 3D reconstruction, image mosaicking,simultaneous localization
and mapping and structure from motion. We consider a special instance in which
there is a dominant ground plane viewed from a parallel viewing
plane above it. Such instances commonly arise, for example, in
aerial photography. Consider a world point and its worst
case reconstruction uncertainty obtained by
merging \emph{all} possible views of chosen from . We first
show that one can pick two views and such that the uncertainty
obtained using only these two views is almost as
good as (i.e. within a small constant factor of) .
Next, we extend the result to the entire ground plane and show
that one can pick a small subset of (which
grows only linearly with the area of ) and still obtain a constant
factor approximation, for every point , to the minimum worst
case estimate obtained by merging all views in . Finally, we
present a multi-resolution view selection method which extends our techniques
to non-planar scenes. We show that the method can produce rich and accurate
dense reconstructions with a small number of views. Our results provide a view
selection mechanism with provable performance guarantees which can drastically
increase the speed of scene reconstruction algorithms. In addition to
theoretical results, we demonstrate their effectiveness in an application where
aerial imagery is used for monitoring farms and orchards
Accelerated gradient methods for total-variation-based CT image reconstruction
Total-variation (TV)-based Computed Tomography (CT) image reconstruction has
shown experimentally to be capable of producing accurate reconstructions from
sparse-view data. In particular TV-based reconstruction is very well suited for
images with piecewise nearly constant regions. Computationally, however,
TV-based reconstruction is much more demanding, especially for 3D imaging, and
the reconstruction from clinical data sets is far from being close to
real-time. This is undesirable from a clinical perspective, and thus there is
an incentive to accelerate the solution of the underlying optimization problem.
The TV reconstruction can in principle be found by any optimization method, but
in practice the large-scale systems arising in CT image reconstruction preclude
the use of memory-demanding methods such as Newton's method. The simple
gradient method has much lower memory requirements, but exhibits slow
convergence. In the present work we consider the use of two accelerated
gradient-based methods, GPBB and UPN, for reducing the number of gradient
method iterations needed to achieve a high-accuracy TV solution in CT image
reconstruction. The former incorporates several heuristics from the
optimization literature such as Barzilai-Borwein (BB) step size selection and
nonmonotone line search. The latter uses a cleverly chosen sequence of
auxiliary points to achieve a better convergence rate. The methods are memory
efficient and equipped with a stopping criterion to ensure that the TV
reconstruction has indeed been found. An implementation of the methods (in C
with interface to Matlab) is available for download from
http://www2.imm.dtu.dk/~pch/TVReg/. We compare the proposed methods with the
standard gradient method, applied to a 3D test problem with synthetic few-view
data. We find experimentally that for realistic parameters the proposed methods
significantly outperform the gradient method.Comment: 4 pages, 2 figure
3D scanning of cultural heritage with consumer depth cameras
Three dimensional reconstruction of cultural heritage objects is an expensive and time-consuming process. Recent consumer real-time depth acquisition devices, like Microsoft Kinect, allow very fast and simple acquisition of 3D views. However 3D scanning with such devices is a challenging task due to the limited accuracy and reliability of the acquired data. This paper introduces a 3D reconstruction pipeline suited to use consumer depth cameras as hand-held scanners for cultural heritage objects. Several new contributions have been made to achieve this result. They include an ad-hoc filtering scheme that exploits the model of the error on the acquired data and a novel algorithm for the extraction of salient points exploiting both depth and color data. Then the salient points are used within a modified version of the ICP algorithm that exploits both geometry and color distances to precisely align the views even when geometry information is not sufficient to constrain the registration. The proposed method, although applicable to generic scenes, has been tuned to the acquisition of sculptures and in this connection its performance is rather interesting as the experimental results indicate
Multi-View Video Packet Scheduling
In multiview applications, multiple cameras acquire the same scene from
different viewpoints and generally produce correlated video streams. This
results in large amounts of highly redundant data. In order to save resources,
it is critical to handle properly this correlation during encoding and
transmission of the multiview data. In this work, we propose a
correlation-aware packet scheduling algorithm for multi-camera networks, where
information from all cameras are transmitted over a bottleneck channel to
clients that reconstruct the multiview images. The scheduling algorithm relies
on a new rate-distortion model that captures the importance of each view in the
scene reconstruction. We propose a problem formulation for the optimization of
the packet scheduling policies, which adapt to variations in the scene content.
Then, we design a low complexity scheduling algorithm based on a trellis search
that selects the subset of candidate packets to be transmitted towards
effective multiview reconstruction at clients. Extensive simulation results
confirm the gain of our scheduling algorithm when inter-source correlation
information is used in the scheduler, compared to scheduling policies with no
information about the correlation or non-adaptive scheduling policies. We
finally show that increasing the optimization horizon in the packet scheduling
algorithm improves the transmission performance, especially in scenarios where
the level of correlation rapidly varies with time
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