19,376 research outputs found
Face image matching using fractal dimension
A new method is presented in this paper for calculating the correspondence between two face images on a pixel by pixel basis. The concept of fractal dimension is used to develop the proposed non-parametric area-based image matching method which achieves a higher proportion of matched pixels for face images than some well-known methods
Image processing for plastic surgery planning
This thesis presents some image processing tools for plastic surgery planning. In particular,
it presents a novel method that combines local and global context in a probabilistic
relaxation framework to identify cephalometric landmarks used in Maxillofacial plastic
surgery. It also uses a method that utilises global and local symmetry to identify abnormalities
in CT frontal images of the human body. The proposed methodologies are
evaluated with the help of several clinical data supplied by collaborating plastic surgeons
The Near-Infrared Number Counts and Luminosity Functions of Local Galaxies
This study presents a wide-field near-infrared (K-band) survey in two fields;
SA 68 and Lynx 2. The survey covers an area of 0.6 deg., complete to
K=16.5. A total of 867 galaxies are detected in this survey of which 175 have
available redshifts. The near-infrared number counts to K=16.5 mag. are
estimated from the complete photometric survey and are found to be in close
agreement with other available studies. The sample is corrected for
incompleteness in redshift space, using selection function in the form of a
Fermi-Dirac distribution. This is then used to estimate the local near-infrared
luminosity function of galaxies. A Schechter fit to the infrared data gives:
M, and Mpc (for H Km/sec/Mpc and q). When
reduced to , this agrees with other available estimates of the local
IRLF. We find a steeper slope for the faint-end of the infrared luminosity
function when compared to previous studies. This is interpreted as due to the
presence of a population of faint but evolved (metal rich) galaxies in the
local Universe. However, it is not from the same population as the faint blue
galaxies found in the optical surveys. The characteristic magnitude
() of the local IRLF indicates that the bright red galaxies ( mag.) have a space density of Mpc and hence,
are not likely to be local objects.Comment: 24 pages, 8 figures, AASTEX 4.0, published in ApJ 492, 45
Class-Based Feature Matching Across Unrestricted Transformations
We develop a novel method for class-based feature matching across large changes in viewing conditions. The method is based on the property that when objects share a similar part, the similarity is preserved across viewing conditions. Given a feature and a training set of object images, we first identify the subset of objects that share this feature. The transformation of the feature's appearance across viewing conditions is determined mainly by properties of the feature, rather than of the object in which it is embedded. Therefore, the transformed feature will be shared by approximately the same set of objects. Based on this consistency requirement, corresponding features can be reliably identified from a set of candidate matches. Unlike previous approaches, the proposed scheme compares feature appearances only in similar viewing conditions, rather than across different viewing conditions. As a result, the scheme is not restricted to locally planar objects or affine transformations. The approach also does not require examples of correct matches. We show that by using the proposed method, a dense set of accurate correspondences can be obtained. Experimental comparisons demonstrate that matching accuracy is significantly improved over previous schemes. Finally, we show that the scheme can be successfully used for invariant object recognition
The correct estimate of the probability of false detection of the matched filter in the detection of weak signals. II. (Further results with application to a set of ALMA and ATCA data)
The matched filter (MF) is one of the most popular and reliable techniques to
the detect signals of known structure and amplitude smaller than the level of
the contaminating noise. Under the assumption of stationary Gaussian noise, MF
maximizes the probability of detection subject to a constant probability of
false detection or false alarm (PFA). This property relies upon a priori
knowledge of the position of the searched signals, which is usually not
available. Recently, it has been shown that when applied in its standard form,
MF may severely underestimate the PFA. As a consequence the statistical
significance of features that belong to noise is overestimated and the
resulting detections are actually spurious. For this reason, an alternative
method of computing the PFA has been proposed that is based on the probability
density function (PDF) of the peaks of an isotropic Gaussian random field. In
this paper we further develop this method. In particular, we discuss the
statistical meaning of the PFA and show that, although useful as a preliminary
step in a detection procedure, it is not able to quantify the actual
reliability of a specific detection. For this reason, a new quantity is
introduced called the specific probability of false alarm (SPFA), which is able
to carry out this computation. We show how this method works in targeted
simulations and apply it to a few interferometric maps taken with the Atacama
Large Millimeter/submillimeter Array (ALMA) and the Australia Telescope Compact
Array (ATCA). We select a few potential new point sources and assign an
accurate detection reliability to these sources.Comment: 28 pages, 20 figures, Astronomy & Astrophysics, Minor changes and
some typos correcte
Simultaneous multi-band detection of Low Surface Brightness galaxies with Markovian modelling
We present an algorithm for the detection of Low Surface Brightness (LSB)
galaxies in images, called MARSIAA (MARkovian Software for Image Analysis in
Astronomy), which is based on multi-scale Markovian modeling. MARSIAA can be
applied simultaneously to different bands. It segments an image into a
user-defined number of classes, according to their surface brightness and
surroundings - typically, one or two classes contain the LSB structures. We
have developed an algorithm, called DetectLSB, which allows the efficient
identification of LSB galaxies from among the candidate sources selected by
MARSIAA. To assess the robustness of our method, the method was applied to a
set of 18 B and I band images (covering 1.3 square degrees in total) of the
Virgo cluster. To further assess the completeness of the results of our method,
both MARSIAA, SExtractor, and DetectLSB were applied to search for (i) mock
Virgo LSB galaxies inserted into a set of deep Next Generation Virgo Survey
(NGVS) gri-band subimages and (ii) Virgo LSB galaxies identified by eye in a
full set of NGVS square degree gri images. MARSIAA/DetectLSB recovered ~20%
more mock LSB galaxies and ~40% more LSB galaxies identified by eye than
SExtractor/DetectLSB. With a 90% fraction of false positives from an entirely
unsupervised pipeline, a completeness of 90% is reached for sources with r_e >
3" at a mean surface brightness level of mu_g=27.7 mag/arcsec^2 and a central
surface brightness of mu^0 g=26.7 mag/arcsec^2. About 10% of the false
positives are artifacts, the rest being background galaxies. We have found our
method to be complementary to the application of matched filters and an
optimized use of SExtractor, and to have the following advantages: it is
scale-free, can be applied simultaneously to several bands, and is well adapted
for crowded regions on the sky.Comment: 39 pages, 18 figures, accepted for publication in A
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