Critical point detection in fluid flow images using dynamical system properties

This paper introduces an algorithm for critical point detection in textured fluid flow images. A new measure is defined, based on dynamical system properties, that identifies candidate critical points in an orientation field. The candidates are verified or rejected based on estimates of the local flow field properties. The algorithm can locate partially occluded and degraded flow structures, and applications of this algorithm to experimental flow imagery are included. The algorithm performance is quantified, and it is compared to other detectors.