43,454 research outputs found
A Cosmic Watershed: the WVF Void Detection Technique
On megaparsec scales the Universe is permeated by an intricate filigree of
clusters, filaments, sheets and voids, the Cosmic Web. For the understanding of
its dynamical and hierarchical history it is crucial to identify objectively
its complex morphological components. One of the most characteristic aspects is
that of the dominant underdense Voids, the product of a hierarchical process
driven by the collapse of minor voids in addition to the merging of large ones.
In this study we present an objective void finder technique which involves a
minimum of assumptions about the scale, structure and shape of voids. Our void
finding method, the Watershed Void Finder (WVF), is based upon the Watershed
Transform, a well-known technique for the segmentation of images. Importantly,
the technique has the potential to trace the existing manifestations of a void
hierarchy. The basic watershed transform is augmented by a variety of
correction procedures to remove spurious structure resulting from sampling
noise. This study contains a detailed description of the WVF. We demonstrate
how it is able to trace and identify, relatively parameter free, voids and
their surrounding (filamentary and planar) boundaries. We test the technique on
a set of Kinematic Voronoi models, heuristic spatial models for a cellular
distribution of matter. Comparison of the WVF segmentations of low noise and
high noise Voronoi models with the quantitatively known spatial characteristics
of the intrinsic Voronoi tessellation shows that the size and shape of the
voids are succesfully retrieved. WVF manages to even reproduce the full void
size distribution function.Comment: 24 pages, 15 figures, MNRAS accepted, for full resolution, see
http://www.astro.rug.nl/~weygaert/tim1publication/watershed.pd
Principal Component Analysis as a Tool for Characterizing Black Hole Images and Variability
We explore the use of principal component analysis (PCA) to characterize
high-fidelity simulations and interferometric observations of the millimeter
emission that originates near the horizons of accreting black holes. We show
mathematically that the Fourier transforms of eigenimages derived from PCA
applied to an ensemble of images in the spatial-domain are identical to the
eigenvectors of PCA applied to the ensemble of the Fourier transforms of the
images, which suggests that this approach may be applied to modeling the sparse
interferometric Fourier-visibilities produced by an array such as the Event
Horizon Telescope (EHT). We also show that the simulations in the spatial
domain themselves can be compactly represented with a PCA-derived basis of
eigenimages allowing for detailed comparisons between variable observations and
time-dependent models, as well as for detection of outliers or rare events
within a time series of images. Furthermore, we demonstrate that the spectrum
of PCA eigenvalues is a diagnostic of the power spectrum of the structure and,
hence, of the underlying physical processes in the simulated and observed
images.Comment: 16 pages, 17 figures, submitted to Ap
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Gap Filling of 3-D Microvascular Networks by Tensor Voting
We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to ïŹll the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated
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