A new approach to the optimization of the extraction of astrometric and
  photometric information from multi-wavelength images in cosmological fields
  This paper describes a new approach to the optimization of information
extraction in multi-wavelength image cubes of cosmological fields. The
objective is to create a framework for the automatic identification and tagging
of sources according to various criteria (isolated source, partially
overlapped, fully overlapped, cross-matched, etc) and to set the basis for the
automatic production of the SEDs (spectral energy distributions) for all
objects detected in the many multi-wavelength images in cosmological fields.In
order to do so, a processing pipeline is designed that combines Voronoi
tessellation, Bayesian cross-matching, and active contours to create a
graph-based representation of the cross-match probabilities. This pipeline
produces a set of SEDs with quality tags suitable for the application of
already-proven data mining methods. The pipeline briefly described here is also
applicable to other astrophysical scenarios such as star forming regions.
