Illustrative Streamline Placement and Visualization

Abstract

Illustrative Streamline Placement and Visualization, IEEE  2008 Pacific Visualization Symposium, Liya Li, Hsien-Hsi Hsieh, and Han-Wei Shen

Publication

Inspired by the abstracting, focusing and explanatory qualities of diagram drawing in art, in this paper we propose a novel seeding strategy to generate representative and illustrative streamlines in 2D vector fields to enforce visual clarity and evidence. A particular focus of our algorithm is to depict the underlying flow patterns effectively and succinctly with a minimum set of streamlines. To achieve this goal, 2D distance fields are generated to encode the distances from each grid point in the field to

the nearby streamlines. A local metric is derived to measure the dissimilarity between the mctors from the original field and an

approximate field computed from the distance fields. A global metric is used to measure the dissimilarity between streamlines based on the local errors to decide whether to drop a new seed at a local point. This process is iterated to generate streamlines until no more streamlines can be found that are dissimilar to the existing ones. We present examples of images generated from our algorithm and report results from qualitative analysis and

user studies.

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The Ohio State University, Department of Computer Science and Engineering

395 Dreese Lab, 2015 Neil Avenue, Columbus OH 43210

Professor Han-Wei Shen

hwshen@cse.ohio-state.edu (V) 614 292 0060  (F) 614 2922911