  We address the problem of one dimensional segment detection and estimation,
in a regression setup. At each point of a fixed or random design, one observes
whether that point belongs to the unknown segment or not, up to some additional
noise. We try to understand what the minimal size of the segment is so it can
be accurately seen by some statistical procedure, and how this minimal size
depends on some a priori knowledge about the location of the unknown segment.
