  Automatic extraction methods typically assume that line segments are
pronounced, thin, few and far between, do not cross each other, and are noise
and clutter-free. Since these assumptions often fail in realistic scenarios,
many line segments are not detected or are fragmented. In more severe cases,
i.e., many who use the Hough Transform, extraction can fail entirely. In this
paper, we propose a method that tackles these issues. Its key aspect is the
combination of thresholded image derivatives obtained with filters of large and
small footprints, which we denote as contextual and local edges, respectively.
Contextual edges are robust to noise and we use them to select valid local
edges, i.e., local edges that are of the same type as contextual ones:
dark-to-bright transition of vice-versa. If the distance between valid local
edges does not exceed a maximum distance threshold, we enforce connectivity by
marking them and the pixels in between as edge points. This originates
connected edge maps that are robust and well localized. We use a powerful
two-sample statistical test to compute contextual edges, which we introduce
briefly, as they are unfamiliar to the image processing community. Finally, we
present experiments that illustrate, with synthetic and real images, how our
method is efficient in extracting complete segments of all lengths and widths
in several situations where current methods fail.
