public documents.sextractor_doc

\chapter{Detection and \index{segmentation} segmentation}
In {\sc SExtractor}, the detection of sources is part of a process called
{\em \index{segmentation} segmentation} in the \index{image} image-processing vocabulary.  Segmentation normally
consists of identifying and separating \index{image} image regions that have different
properties (brightness, \index{colour} colour, texture...) or are delineated by edges. In
the astronomical context, the \index{segmentation} segmentation process consists of separating
objects from the sky background. This is however a somewhat imprecise
definition, as astronomical sources have, on the \index{image} images --- and even often
physically ---, no clear \index{boundaries} boundaries, and may overlap. We shall therefore use
the following working definition of an object in {\sc SExtractor}: a group of
pixels selected through some detection process and for which the flux
contribution of an astronomical source is believed to be dominant over that
of other objects. Note that this \index{mean} means that a simple $x,y$ position vector
alone cannot be handled by {\sc SExtractor} as a detection: most measurement
routines require some rough shape information about the objects.
 
Segmentation in {\sc SExtractor} is achieved through a very simple
\index{threshold} thresholding process: a group of connected pixels that exceed some \index{threshold} threshold
above the background is identified as a detection.  But things are a little
bit more complicated in practice. First, on most astronomical \index{image} images, the
background is not constant over the frame, and its determination can be
ambiguous in crowded regions. Second, the software has to operate on noisy
data, and some filtering adapted to the characteristics of the \index{image} image has to
be applied prior to detection, to reduce the contamination by noise
peaks. Third, many sources that overlap on the \index{image} image are unlikely to be
detected separately with a single detection \index{threshold} threshold, and require a
de-blending procedure, which is actually \index{multi-thresholding} multi-thresholding in {\sc
  SExtractor}. Each of these points will now be described in greater detail
below. It is worth mentioning here that these 3 difficulties could, to a
large extent, be bypassed using a \index{wavelet} wavelet decomposition (e.g. Bijaoui \etal
1998). Although such an algorithm might be implemented in a future version of
{\sc SExtractor}, current constraints in processing speed, available \index{memory} memory
(processing of gigantic \index{image} images) often make the ``pedestrian approach'' still
more interesting in the case of large scale surveys. \gam{Remove or update
  last two sentences on \index{wavelet} \index{wavelets} wavelets?}