3,419 research outputs found
Automatic quantitative morphological analysis of interacting galaxies
The large number of galaxies imaged by digital sky surveys reinforces the
need for computational methods for analyzing galaxy morphology. While the
morphology of most galaxies can be associated with a stage on the Hubble
sequence, morphology of galaxy mergers is far more complex due to the
combination of two or more galaxies with different morphologies and the
interaction between them. Here we propose a computational method based on
unsupervised machine learning that can quantitatively analyze morphologies of
galaxy mergers and associate galaxies by their morphology. The method works by
first generating multiple synthetic galaxy models for each galaxy merger, and
then extracting a large set of numerical image content descriptors for each
galaxy model. These numbers are weighted using Fisher discriminant scores, and
then the similarities between the galaxy mergers are deduced using a variation
of Weighted Nearest Neighbor analysis such that the Fisher scores are used as
weights. The similarities between the galaxy mergers are visualized using
phylogenies to provide a graph that reflects the morphological similarities
between the different galaxy mergers, and thus quantitatively profile the
morphology of galaxy mergers.Comment: Astronomy & Computing, accepte
Improved texture image classification through the use of a corrosion-inspired cellular automaton
In this paper, the problem of classifying synthetic and natural texture
images is addressed. To tackle this problem, an innovative method is proposed
that combines concepts from corrosion modeling and cellular automata to
generate a texture descriptor. The core processes of metal (pitting) corrosion
are identified and applied to texture images by incorporating the basic
mechanisms of corrosion in the transition function of the cellular automaton.
The surface morphology of the image is analyzed before and during the
application of the transition function of the cellular automaton. In each
iteration the cumulative mass of corroded product is obtained to construct each
of the attributes of the texture descriptor. In a final step, this texture
descriptor is used for image classification by applying Linear Discriminant
Analysis. The method was tested on the well-known Brodatz and Vistex databases.
In addition, in order to verify the robustness of the method, its invariance to
noise and rotation were tested. To that end, different variants of the original
two databases were obtained through addition of noise to and rotation of the
images. The results showed that the method is effective for texture
classification according to the high success rates obtained in all cases. This
indicates the potential of employing methods inspired on natural phenomena in
other fields.Comment: 13 pages, 14 figure
Recommended from our members
An investigation into the use of genetic algorithms for shape recognition
The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated
Analysing Large Scale Structure: I. Weighted Scaling Indices and Constrained Randomisation
The method of constrained randomisation is applied to three-dimensional
simulated galaxy distributions. With this technique we generate for a given
data set surrogate data sets which have the same linear properties as the
original data whereas higher order or nonlinear correlations are not preserved.
The analysis of the original and surrogate data sets with measures, which are
sensitive to nonlinearities, yields information about the existence of
nonlinear correlations in the data. We demonstrate how to generate surrogate
data sets from a given point distribution, which have the same linear
properties (power spectrum) as well as the same density amplitude distribution.
We propose weighted scaling indices as a nonlinear statistical measure to
quantify local morphological elements in large scale structure. Using
surrogates is is shown that the data sets with the same 2-point correlation
functions have slightly different void probability functions and especially a
different set of weighted scaling indices. Thus a refined analysis of the large
scale structure becomes possible by calculating local scaling properties
whereby the method of constrained randomisation yields a vital tool for testing
the performance of statistical measures in terms of sensitivity to different
topological features and discriminative power.Comment: 10 pages, 5 figures, accepted for publication in MNRA
- …