Abstract
In any image segmentation task, noise must be separated from
the actual information and the relevant pixels grouped into objects of
interest, on which measures can later be applied. This should be done
efficiently on large astronomical surveys with floating point datasets
with resolution of the order of Gigapixels. We illustrate in this paper how
the combination of two techniques presented in previous works can help
in this task. We summarise the benefits and initial outcomes of
combining together a parallel algorithm to build max-trees of floating
point data sets and a connected attribute filter that uses a
statistical approach to identify structures due to noise and to
perform segmentation on 3D radio cubes.
the actual information and the relevant pixels grouped into objects of
interest, on which measures can later be applied. This should be done
efficiently on large astronomical surveys with floating point datasets
with resolution of the order of Gigapixels. We illustrate in this paper how
the combination of two techniques presented in previous works can help
in this task. We summarise the benefits and initial outcomes of
combining together a parallel algorithm to build max-trees of floating
point data sets and a connected attribute filter that uses a
statistical approach to identify structures due to noise and to
perform segmentation on 3D radio cubes.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2014 conference on Big Data from Space BiDS14 |
| Publisher | Publications Office of the European Union |
| Pages | 232-235 |
| Number of pages | 4 |
| Publication status | Published - 2014 |