A Fast, Square-Spiral, Finite Element Interest Point Detector

Scale-invariant interest point detection is crucial for many computer vision tasks in dynamic environments, such as manufacturing, where identifying recurring visual landmarks is essential for process monitoring. However, existing solutions, including the Scale-Invariant Feature Transform and its descendants, incur high computational costs due to operations across multiple scales. Drawing inspiration from the Finite Element Scale-Invariant Detector, this paper introduces a novel square-spiral derivative that integrates a vectorized image addressing scheme with complementary processing techniques to enhance runtime performance. Experimental evaluations demonstrate that the proposed approach remains robust under moderate geometric and photometric transformations, achieving competitive performance compared to widely used detectors in conditions that resemble those in controlled dynamic environments. Furthermore, it significantly reduces computational overhead, providing an efficient solution for vision-based manufacturing tasks.