  The micro-structure of most of the engineering alloys contains some
inclusions and precipitates, which may affect their properties, therefore it is
crucial to characterize them. In this work we focus on the development of a
state-of-the-art artificial intelligence model for Anomaly Detection named
MLography to automatically quantify the degree of anomaly of impurities in
alloys. For this purpose, we introduce several anomaly detection measures:
Spatial, Shape and Area anomaly, that successfully detect the most anomalous
objects based on their objective, given that the impurities were already
labeled. The first two measures quantify the degree of anomaly of each object
by how each object is distant and big compared to its neighborhood, and by the
abnormally of its own shape respectively. The last measure, combines the former
two and highlights the most anomalous regions among all input images, for later
(physical) examination. The performance of the model is presented and analyzed
based on few representative cases. We stress that although the models presented
here were developed for metallography analysis, most of them can be generalized
to a wider set of problems in which anomaly detection of geometrical objects is
desired. All models as well as the data-set that was created for this work, are
publicly available at: https://github.com/matanr/MLography.
