35,014 research outputs found
A Fast Algorithm for Cosmic Rays Removal from Single Images
We present a method for detecting cosmic rays in single images. The algorithm
is based on simple analysis of the histogram of the image data and does not use
any modeling of the picture of the object. It does not require a good signal to
noise ratio in the image data. Identification of multiple-pixel cosmic-ray hits
is realized by running the procedure for detection and replacement iteratively.
The tests performed by us, show that the method is very effective, when applied
to the images with the spectroscopic data. It is also very fast in comparison
with other single image algorithms found in astronomical data processing
packages. Practical implementation and examples of application are presented.Comment: 12 pages, 5 figures, uses aastex.cl
Reference-less detection, astrometry, and photometry of faint companions with adaptive optics
We propose a complete framework for the detection, astrometry, and photometry
of faint companions from a sequence of adaptive optics corrected short
exposures. The algorithms exploit the difference in statistics between the
on-axis and off-axis intensity. Using moderate-Strehl ratio data obtained with
the natural guide star adaptive optics system on the Lick Observatory's 3-m
Shane Telescope, we compare these methods to the standard approach of PSF
fitting. We give detection limits for the Lick system, as well as a first guide
to expected accuracy of differential photometry and astrometry with the new
techniques. The proposed approach to detection offers a new way of determining
dynamic range, while the new algorithms for differential photometry and
astrometry yield accurate results for very faint and close-in companions where
PSF fitting fails. All three proposed algorithms are self-calibrating, i.e.
they do not require observation of a calibration star thus improving the
observing efficiency.Comment: Astrophysical Journal 698 (2009) 28-4
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
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