SAFFIRE: System for Autonomous Feature Filtering and Intelligent ROI Estimation
This work addresses the problem of subjective and expert-dependent anchor pattern identification for automated product inspection systems, benefiting machine vision engineers.
The SAFFIRE framework automatically extracts a dominant recurrent image pattern from a set of image samples to eliminate pose variations, which is crucial for automated product inspection. It provides unsupervised identification of an optimal anchor pattern, removing the need for subjective and expert user input.
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.