11,149 research outputs found
Evaluation of Single-Chip, Real-Time Tomographic Data Processing on FPGA - SoC Devices
A novel approach to tomographic data processing has been developed and
evaluated using the Jagiellonian PET (J-PET) scanner as an example. We propose
a system in which there is no need for powerful, local to the scanner
processing facility, capable to reconstruct images on the fly. Instead we
introduce a Field Programmable Gate Array (FPGA) System-on-Chip (SoC) platform
connected directly to data streams coming from the scanner, which can perform
event building, filtering, coincidence search and Region-Of-Response (ROR)
reconstruction by the programmable logic and visualization by the integrated
processors. The platform significantly reduces data volume converting raw data
to a list-mode representation, while generating visualization on the fly.Comment: IEEE Transactions on Medical Imaging, 17 May 201
Geometric reconstruction methods for electron tomography
Electron tomography is becoming an increasingly important tool in materials
science for studying the three-dimensional morphologies and chemical
compositions of nanostructures. The image quality obtained by many current
algorithms is seriously affected by the problems of missing wedge artefacts and
nonlinear projection intensities due to diffraction effects. The former refers
to the fact that data cannot be acquired over the full tilt range;
the latter implies that for some orientations, crystalline structures can show
strong contrast changes. To overcome these problems we introduce and discuss
several algorithms from the mathematical fields of geometric and discrete
tomography. The algorithms incorporate geometric prior knowledge (mainly
convexity and homogeneity), which also in principle considerably reduces the
number of tilt angles required. Results are discussed for the reconstruction of
an InAs nanowire
Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing
Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen
Generic iterative subset algorithms for discrete tomography
AbstractDiscrete tomography deals with the reconstruction of images from their projections where the images are assumed to contain only a small number of grey values. In particular, there is a strong focus on the reconstruction of binary images (binary tomography). A variety of binary tomography problems have been considered in the literature, each using different projection models or additional constraints. In this paper, we propose a generic iterative reconstruction algorithm that can be used for many different binary reconstruction problems. In every iteration, a subproblem is solved based on at most two of the available projections. Each of the subproblems can be solved efficiently using network flow methods. We report experimental results for various reconstruction problems. Our results demonstrate that the algorithm is capable of reconstructing complex objects from a small number of projections
Classical Planning in Deep Latent Space
Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose Latplan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), Latplan learns a complete propositional PDDL
action model of the environment. Later, when a pair of images representing the
initial and the goal states (planning inputs) is given, Latplan finds a plan to
the goal state in a symbolic latent space and returns a visualized plan
execution. We evaluate Latplan using image-based versions of 6 planning
domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of
LightsOut.Comment: Under review at Journal of Artificial Intelligence Research (JAIR
On the nature of the Herbig B[e] star binary system V921 Scorpii: Discovery of a close companion and relation to the large-scale bipolar nebula
Belonging to the group of B[e] stars, V921 Scorpii is associated with a
strong infrared excess and permitted and forbidden line emission, indicating
the presence of low- and high-density circumstellar gas and dust. Many aspects
of V921 Sco and other B[e] stars still remain mysterious, including their
evolutionary state and the physical conditions resulting in the class-defining
characteristics. In this paper, we employ VLTI/AMBER spectro-interferometry in
order to reconstruct high-resolution (lambda/2B=0.0013") model-independent
interferometric images for three wavelength bands around 1.65, 2.0, and 2.3
micrometer. In our images, we discover a close (25.0+/-0.8 milliarcsecond,
corresponding to 29+/-0.9 AU at 1.15 kpc) companion around V921 Sco. Between
two epochs in 2008 and 2009, we measure orbital motion of 7 degrees, implying
an orbital period of about 35 years (for a circular orbit). Around the primary
star, we detect a disk-like structure with indications for a radial temperature
gradient. The polar axis of this AU-scale disk is aligned with the
arcminute-scale bipolar nebula in which V921 Sco is embedded. Using
Magellan/IMACS imaging, we detect multi-layered arc-shaped sub-structure in the
nebula, suggesting episodic outflow activity from the system with a period of
about 25 years, roughly matching the estimated orbital period of the companion.
Our study supports the hypothesis that the B[e] phenomenon is related to
dynamical interaction in a close binary system.Comment: 7 pages, 2 figures, accepted by The Astrophysical Journal Letter
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