  (Abridged) We present a new method for detecting and measuring compact
sources in conditions of intense, and highly variable, fore/background. While
all most commonly used packages carry out the source detection over the signal
image, our proposed method builds from the measured image a "curvature" image
by double-differentiation in four different directions. In this way point-like
as well as resolved, yet relatively compact, objects are easily revealed while
the slower varying fore/background is greatly diminished. Candidate sources are
then identified by looking for pixels where the curvature exceeds, in absolute
terms, a given threshold; the methodology easily allows us to pinpoint
breakpoints in the source brightness profile and then derive reliable guesses
for the sources extent. Identified peaks are fit with 2D elliptical Gaussians
plus an underlying planar inclined plateau, with mild constraints on size and
orientation. Mutually contaminating sources are fit with multiple Gaussians
simultaneously using flexible constraints. We ran our method on simulated
large-scale fields with 1000 sources of different peak flux overlaid on a
realistic realization of diffuse background. We find detection rates in excess
of 90% for sources with peak fluxes above the 3-sigma signal noise limit; for
about 80% of the sources the recovered peak fluxes are within 30% of their
input values.
