10,166 research outputs found
The Multiscale Morphology Filter: Identifying and Extracting Spatial Patterns in the Galaxy Distribution
We present here a new method, MMF, for automatically segmenting cosmic
structure into its basic components: clusters, filaments, and walls.
Importantly, the segmentation is scale independent, so all structures are
identified without prejudice as to their size or shape. The method is ideally
suited for extracting catalogues of clusters, walls, and filaments from samples
of galaxies in redshift surveys or from particles in cosmological N-body
simulations: it makes no prior assumptions about the scale or shape of the
structures.}Comment: Replacement with higher resolution figures. 28 pages, 17 figures. For
Full Resolution Version see:
http://www.astro.rug.nl/~weygaert/tim1publication/miguelmmf.pd
Multiscale Astronomical Image Processing Based on Nonlinear Partial Differential Equations
Astronomical applications of recent advances in the field of nonastronomical image processing are presented. These innovative methods, applied to multiscale astronomical images, increase signal-to-noise ratio, do not smear point sources or extended diffuse structures, and are thus a highly useful preliminary step for detection of different features including point sources, smoothing of clumpy data, and removal of contaminants from background maps. We show how the new methods, combined with other algorithms of image processing, unveil fine diffuse structures while at the same time enhance detection of localized objects, thus facilitating interactive morphology studies and paving the way for the automated recognition and classification of different features. We have also developed a new application framework for astronomical image processing that implements some recent advances made in computer vision and modern image processing, along with original algorithms based on nonlinear partial differential equations. The framework enables the user to easily set up and customize an image-processing pipeline interactively; it has various common and new visualization features and provides access to many astronomy data archives. Altogether, the results presented here demonstrate the first implementation of a novel synergistic approach based on integration of image processing, image visualization, and image quality assessment
A Scale-Space Medialness Transform Based on Boundary Concordance Voting
The Concordance-based Medial Axis Transform (CMAT) presented in this paper is a multiscale medial axis (MMA) algorithm that computes the medial response from grey-level boundary measures. This non-linear operator responds only to symmetric structures, overcoming the limitations of linear medial operators which create “side-lobe” responses for symmetric structures and respond to edge structures. In addition, the spatial localisation of the medial axis and the identification of object width is improved in the CMAT algorithm compared with linear algorithms. The robustness of linear medial operators to noise is preserved in our algorithm. The effectiveness of the CMAT is accredited to the concordance property described in this paper. We demonstrate the performance of this method with test figures used by other authors and medical images that are relatively complex in structure. In these complex images the benefit of the improved response of our non-linear operator is clearly visible
Fractal Descriptors in the Fourier Domain Applied to Color Texture Analysis
The present work proposes the development of a novel method to provide
descriptors for colored texture images. The method consists in two steps. In
the first, we apply a linear transform in the color space of the image aiming
at highlighting spatial structuring relations among the color of pixels. In a
second moment, we apply a multiscale approach to the calculus of fractal
dimension based on Fourier transform. From this multiscale operation, we
extract the descriptors used to discriminate the texture represented in digital
images. The accuracy of the method is verified in the classification of two
color texture datasets, by comparing the performance of the proposed technique
to other classical and state-of-the-art methods for color texture analysis. The
results showed an advantage of almost 3% of the proposed technique over the
second best approach.Comment: Chaos, Volume 21, Issue 4, 201
Unfolding the Hierarchy of Voids
We present a framework for the hierarchical identification and
characterization of voids based on the Watershed Void Finder. The Hierarchical
Void Finder is based on a generalization of the scale space of a density field
invoked in order to trace the hierarchical nature and structure of cosmological
voids. At each level of the hierarchy, the watershed transform is used to
identify the voids at that particular scale. By identifying the overlapping
regions between watershed basins in adjacent levels, the hierarchical void tree
is constructed. Applications on a hierarchical Voronoi model and on a set of
cosmological simulations illustrate its potential.Comment: 5 pages, 2 figure
Weak Lensing Mass Reconstruction using Wavelets
This paper presents a new method for the reconstruction of weak lensing mass
maps. It uses the multiscale entropy concept, which is based on wavelets, and
the False Discovery Rate which allows us to derive robust detection levels in
wavelet space. We show that this new restoration approach outperforms several
standard techniques currently used for weak shear mass reconstruction. This
method can also be used to separate E and B modes in the shear field, and thus
test for the presence of residual systematic effects. We concentrate on large
blind cosmic shear surveys, and illustrate our results using simulated shear
maps derived from N-Body Lambda-CDM simulations with added noise corresponding
to both ground-based and space-based observations.Comment: Accepted manuscript with all figures can be downloaded at:
http://jstarck.free.fr/aa_wlens05.pdf and software can be downloaded at
http://jstarck.free.fr/mrlens.htm
Sunyaev-Zel'dovich clusters reconstruction in multiband bolometer camera surveys
We present a new method for the reconstruction of Sunyaev-Zel'dovich (SZ)
galaxy clusters in future SZ-survey experiments using multiband bolometer
cameras such as Olimpo, APEX, or Planck. Our goal is to optimise SZ-Cluster
extraction from our observed noisy maps. We wish to emphasize that none of the
algorithms used in the detection chain is tuned on prior knowledge on the SZ
-Cluster signal, or other astrophysical sources (Optical Spectrum, Noise
Covariance Matrix, or covariance of SZ Cluster wavelet coefficients). First, a
blind separation of the different astrophysical components which contribute to
the observations is conducted using an Independent Component Analysis (ICA)
method. Then, a recent non linear filtering technique in the wavelet domain,
based on multiscale entropy and the False Discovery Rate (FDR) method, is used
to detect and reconstruct the galaxy clusters. Finally, we use the Source
Extractor software to identify the detected clusters. The proposed method was
applied on realistic simulations of observations. As for global detection
efficiency, this new method is impressive as it provides comparable results to
Pierpaoli et al. method being however a blind algorithm. Preprint with full
resolution figures is available at the URL:
w10-dapnia.saclay.cea.fr/Phocea/Vie_des_labos/Ast/ast_visu.php?id_ast=728Comment: Submitted to A&A. 32 Pages, text onl
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