Filament Identification through Mathematical Morphology
  We present a new algorithm for detecting filamentary structure FilFinder. The
algorithm uses the techniques of mathematical morphology for filament
identification, presenting a complementary approach to current algorithms which
use matched filtering or critical manifolds. Unlike other methods, FilFinder
identifies filaments over a wide dynamic range in brightness. We apply the new
algorithm to far infrared imaging data of dust emission released by the
Herschel Gould Belt Survey team. Our preliminary analysis characterizes both
filaments and fainter striations. We find a typical filament width of 0.09 pc
across the sample, but the brightness varies from cloud to cloud. Several
regions show a bimodal filament brightness distribution, with the bright mode
(filaments) being an order of magnitude brighter than the faint mode
(striations). Using the Rolling Hough Transform, we characterize the
orientations of the striations in the data, finding preferred directions that
agree with magnetic field direction where data are available. There is a
suggestive but noisy correlation between typical filament brightness and
literature values of the star formation rates for clouds in the Gould Belt.
