  This work introduces a new framework, named SAFFIRE, to automatically extract
a dominant recurrent image pattern from a set of image samples. Such a pattern
shall be used to eliminate pose variations between samples, which is a common
requirement in many computer vision and machine learning tasks. The framework
is specialized here in the context of a machine vision system for automated
product inspection. Here, it is customary to ask the user for the
identification of an anchor pattern, to be used by the automated system to
normalize data before further processing. Yet, this is a very sensitive
operation which is intrinsically subjective and requires high expertise.
Hereto, SAFFIRE provides a unique and disruptive framework for unsupervised
identification of an optimal anchor pattern in a way which is fully transparent
to the user. SAFFIRE is thoroughly validated on several realistic case studies
for a machine vision inspection pipeline.
