  We review the broad variety of methods that have been proposed for anomaly
detection in images. Most methods found in the literature have in mind a
particular application. Yet we show that the methods can be classified mainly
by the structural assumption they make on the "normal" image. Five different
structural assumptions emerge. Our analysis leads us to reformulate the best
representative algorithms by attaching to them an a contrario detection that
controls the number of false positives and thus derive universal detection
thresholds. By combining the most general structural assumptions expressing the
background's normality with the best proposed statistical detection tools, we
end up proposing generic algorithms that seem to generalize or reconcile most
methods. We compare the six best representatives of our proposed classes of
algorithms on anomalous images taken from classic papers on the subject, and on
a synthetic database. Our conclusion is that it is possible to perform
automatic anomaly detection on a single image.
