SGN-90206 International Doctoral Seminar: Signal Processing Journal Club
Sparse representations are one of the most important models in
signal/image processing. These models were originally introduced for
descriptive tasks such as denoising/regression, but more recently
discriminative tasks, classification in particular, have also been
addressed in the sparse representation literature. During this talk, I
will present our work on exploiting sparse representation for
change/anomaly detection, namely, the identification of data departing
from the normal (stationary) conditions. We assume that normal data
admits a sparse representation w.r.t. a suitable dictionary, thus that
they are well approximated in a union of low-dimensional subspaces.
Performing change/anomaly detection then corresponds to identifying
data that departs from these subspaces, both because of a permanent
shift in the data-generating process (change-detection) or because of
a sporadic event (anomaly detection).
I will illustrate the effectiveness of this approach in two
application scenarios. The first consists of an environmental
monitoring application, where nodes of a sensor network deployed over
a rock face acquires acoustic emissions (bursts). The stream of bursts
is then analyzed to detect structural changes which might be
associated with macroscopic variation in the monitored phenomenon,
eventually leading to a rock collapse. The second consists of an
industrial monitoring application, where a scanning electron
microscope (SEM) is used to supervise the production of nanofibres.
Here, we address the problem of detecting anomalous patterns in the
acquired images, which might indicate a degradation in the quality of
the produced materials.