45,566 research outputs found
Automatic detection of arcs and arclets formed by gravitational lensing
We present an algorithm developed particularly to detect gravitationally
lensed arcs in clusters of galaxies. This algorithm is suited for automated
surveys as well as individual arc detections. New methods are used for image
smoothing and source detection. The smoothing is performed by so-called
anisotropic diffusion, which maintains the shape of the arcs and does not
disperse them. The algorithm is much more efficient in detecting arcs than
other source finding algorithms and the detection by eye.Comment: A&A in press, 12 pages, 16 figure
On the segmentation of astronomical images via level-set methods
Astronomical images are of crucial importance for astronomers since they
contain a lot of information about celestial bodies that can not be directly
accessible. Most of the information available for the analysis of these objects
starts with sky explorations via telescopes and satellites. Unfortunately, the
quality of astronomical images is usually very low with respect to other real
images and this is due to technical and physical features related to their
acquisition process. This increases the percentage of noise and makes more
difficult to use directly standard segmentation methods on the original image.
In this work we will describe how to process astronomical images in two steps:
in the first step we improve the image quality by a rescaling of light
intensity whereas in the second step we apply level-set methods to identify the
objects. Several experiments will show the effectiveness of this procedure and
the results obtained via various discretization techniques for level-set
equations.Comment: 24 pages, 59 figures, paper submitte
A Neural Network Architecture for Figure-ground Separation of Connected Scenic Figures
A neural network model, called an FBF network, is proposed for automatic parallel separation of multiple image figures from each other and their backgrounds in noisy grayscale or multi-colored images. The figures can then be processed in parallel by an array of self-organizing Adaptive Resonance Theory (ART) neural networks for automatic target recognition. An FBF network can automatically separate the disconnected but interleaved spirals that Minsky and Papert introduced in their book Perceptrons. The network's design also clarifies why humans cannot rapidly separate interleaved spirals, yet can rapidly detect conjunctions of disparity and color, or of disparity and motion, that distinguish target figures from surrounding distractors. Figure-ground separation is accomplished by iterating operations of a Feature Contour System (FCS) and a Boundary Contour System (BCS) in the order FCS-BCS-FCS, hence the term FBF, that have been derived from an analysis of biological vision. The FCS operations include the use of nonlinear shunting networks to compensate for variable illumination and nonlinear diffusion networks to control filling-in. A key new feature of an FBF network is the use of filling-in for figure-ground separation. The BCS operations include oriented filters joined to competitive and cooperative interactions designed to detect, regularize, and complete boundaries in up to 50 percent noise, while suppressing the noise. A modified CORT-X filter is described which uses both on-cells and off-cells to generate a boundary segmentation from a noisy image.Air Force Office of Scientific Research (90-0175); Army Research Office (DAAL-03-88-K0088); Defense Advanced Research Projects Agency (90-0083); Hughes Research Laboratories (S1-804481-D, S1-903136); American Society for Engineering Educatio
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