  Vortices are studied in various scientific disciplines, offering insights
into fluid flow behavior. Visualizing the boundary of vortices is crucial for
understanding flow phenomena and detecting flow irregularities. This paper
addresses the challenge of accurately extracting vortex boundaries using deep
learning techniques. While existing methods primarily train on velocity
components, we propose a novel approach incorporating particle trajectories
(streamlines or pathlines) into the learning process. By leveraging the
regional/local characteristics of the flow field captured by streamlines or
pathlines, our methodology aims to enhance the accuracy of vortex boundary
extraction.
