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?}