Similarity Measures for Enhancing Interactive Streamline Seeding

Abstract

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.

Expressive seeding of multiple stream surfaces for interactive flow exploration

“…An overview of the topic has been presented by Moberts et al [2]. McLoughlin et al [5] deal with the problem of distributing streamlines along a rake. For each streamline, they compute a signature based on three shape descriptors: curvature, torsion and tortuosity.…”

“…Even the Hausdorff distance could be replaced with any other similarity metric for streamlines. For demonstration purposes, we have implemented the similarity metric by McLoughlin et al [5], which essentially analyzes the shapes of the streamlines according to curvature and torsion. Fig.…”

“…In flow visualization, similarity measures for streamlines are a common way to characterize the diverse aspects of the flow behavior [2][3][4][5]. We adopt this strategy as well.…”

FlowString: Partial Streamline Matching Using Shape Invariant Similarity Measure for Exploratory Flow Visualization

“…Examples include shape and orientation [3], and local and global geometric properties [16]. Feature distributions of integral curves are less sensitive to noise in the data and sharp turns or twists at certain locations [21,11,17,10,12]. Therefore, they are often used for more robust similarity measuring.…”

Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop

“…Similarly, the direction is computed based on the vector from the start point to the end point of each point set, from which we calculate the difference value. For computing curvature and tortuosity, we adapt the approach taken by McLoughlin et al [26] for computing dissimilarity in interactive streamline seeding. Curvature is computed by v × a/|v| 3 where v is the local velocity and a is the local acceleration computed by multiplying the local velocity gradient with the local velocity.…”