63,107 research outputs found
Inclination-Independent Galaxy Classification
We present a new method to classify galaxies from large surveys like the
Sloan Digital Sky Survey using inclination-corrected concentration,
inclination-corrected location on the color-magnitude diagram, and apparent
axis ratio. Explicitly accounting for inclination tightens the distribution of
each of these parameters and enables simple boundaries to be drawn that
delineate three different galaxy populations: Early-type galaxies, which are
red, highly concentrated, and round; Late-type galaxies, which are blue, have
low concentrations, and are disk dominated; and Intermediate-type galaxies,
which are red, have intermediate concentrations, and have disks. We have
validated our method by comparing to visual classifications of high-quality
imaging data from the Millennium Galaxy Catalogue. The inclination correction
is crucial to unveiling the previously unrecognized Intermediate class.
Intermediate-type galaxies, roughly corresponding to lenticulars and early
spirals, lie on the red sequence. The red sequence is therefore composed of two
distinct morphological types, suggesting that there are two distinct mechanisms
for transiting to the red sequence. We propose that Intermediate-type galaxies
are those that have lost their cold gas via strangulation, while Early-type
galaxies are those that have experienced a major merger that either consumed
their cold gas, or whose merger progenitors were already devoid of cold gas
(the ``dry merger'' scenario).Comment: Accepted for publication in ApJ. 7 pages in emulateap
Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition
This paper presents a comparative study of two different methods, which are
based on fusion and polar transformation of visual and thermal images. Here,
investigation is done to handle the challenges of face recognition, which
include pose variations, changes in facial expression, partial occlusions,
variations in illumination, rotation through different angles, change in scale
etc. To overcome these obstacles we have implemented and thoroughly examined
two different fusion techniques through rigorous experimentation. In the first
method log-polar transformation is applied to the fused images obtained after
fusion of visual and thermal images whereas in second method fusion is applied
on log-polar transformed individual visual and thermal images. After this step,
which is thus obtained in one form or another, Principal Component Analysis
(PCA) is applied to reduce dimension of the fused images. Log-polar transformed
images are capable of handling complicacies introduced by scaling and rotation.
The main objective of employing fusion is to produce a fused image that
provides more detailed and reliable information, which is capable to overcome
the drawbacks present in the individual visual and thermal face images.
Finally, those reduced fused images are classified using a multilayer
perceptron neural network. The database used for the experiments conducted here
is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database
benchmark thermal and visual face images. The second method has shown better
performance, which is 95.71% (maximum) and on an average 93.81% as correct
recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in
Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11
- 15, 201
Experiences in Automatic Keywording of Particle Physics Literature
Attributing keywords can assist in the classification and retrieval of documents in the particle physics literature. As information services face a future with less available manpower and more and more documents being written, the possibility of keyword attribution being assisted by automatic classification software is explored. A project being carried out at CERN (the European Laboratory for Particle Physics) for the development and integration of automatic keywording is described
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