SAFFIRE: System for Autonomous Feature Filtering and Intelligent ROI Estimation

TL;DR

SAFFIRE is an innovative framework that automatically identifies dominant image patterns to normalize pose variations, enhancing automation and reducing user subjectivity in machine vision inspection tasks.

Contribution

It introduces a fully unsupervised, transparent method for selecting optimal anchor patterns, eliminating the need for expert user input in product inspection systems.

Findings

Successfully validated on multiple real-world case studies.

Improves automation and objectivity in pattern normalization.

Reduces reliance on expert knowledge for pattern selection.

Abstract

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.