  The H.E.S.S. Galactic Plane Survey (HGPS) represents one of the most
sensitive surveys of the Galactic Plane at very high energies (VHE, 0.1-100
TeV). However the source detection algorithm of the HGPS pipeline is not
well-suited for complex regions, including sources with shell-like
morphologies. As an alternative and complementary approach, we have
investigated blind search methods for VHE gamma-ray source detection based on
well-known and widely used image processing and pattern recognition techniques.
Our goal is to build in a short amount of computational time a list of
potentially valuable objects without prior case-specific morphological
assumptions. We aim to classify and rank the detected objects in order to
identify only the most promising source candidates for further
multi-wavelength-association searches, dedicated analyses, or deeper
observations. In the approach proposed, we extract sparse and pertinent
structural information from the significance maps using a edge detection
operator. We then apply a Hough circle transform and detect a collection of
objects as local maxima in the Hough space. On the basis of morphological
parameters we can characterize different object classes. Classification can be
used to identify valuable source candidates sharing the characteristics of
well-known sources. We show that using these pattern recognition techniques we
can detect objects with partial circular symmetry irrespective of a
morphological template (e.g. point-like, Gaussian-like, or shell-like). All the
shell-type supernova remnants (SNRs) catalogued in the HGPS are associated with
at least one detected object. Catalogue cross-matches indicate that several
detected objects not catalogued in the HGPS are spatially coincident with
multi-wavelength counterparts. Further investigation on the most promising
candidates will be conducted in dedicated follow-up analyses.
