Effective exploration of spatiotemporal volumetric data sets remains a key challenge in scientific visualization. Although great advances have been made over the years, existing solutions typically focus on only one or two aspects of data analysis and visualization. A streamlined workflow for analyzing time-varying data in a comprehensive and unified manner is still missing. Towards this goal, we present a novel approach for time-varying data visualization that encompasses keyframe identification, feature extraction and tracking under a single, unified framework. At the heart of our approach lies in the GPU-accelerated BlockMatch method, a dense block correspondence technique that extends the PatchMatch method from 2D pixels to 3D voxels. Based on the results of dense correspondence, we are able to identify keyframes from the time sequence using k-medoids clustering along with a bidirectional similarity measure. Furthermore, in conjunction with the graph cut algorithm, this framework enables us to perform fine-grained feature extraction and tracking. We tested our approach using several time-varying data sets to demonstrate its effectiveness and utility.

This paper describes an effort to create new visualizations by exploiting hierarchical scalar topology. First, we build a hierarchical topology through synchronously constructing and simplifying Contour Tree (CT) and Morse-Smale (MS) complex of scalar fields. We then introduce three algorithms based on the hierarchical topology: (1) topology-based multi-resolution contouring — an overview provided for a scalar field by extracting iso-values from the simplified CT and tracing approximate contours across the MS complex cells; (2) topology based spaghetti plots for uncertainty — a seeding scheme based on the hierarchical topology for visualizing uncertainty among ensemble scalar data; (3) virtual ribbons — a new scheme for visualizing multivariate data invented by overlapping visual ribbons which encode the scalar variation of a region covered by uniform contours. We compare the new approaches with current alternatives.

This paper presents a new streamline placement algorithm that produces evenly spaced long streamlines while preserving topological features of a flow field. Singularities and separatrices are extracted to decompose the flow field into topological regions. In each region, a seeding path is selected from a set of streamlines integrated in the orthogonal flow field. The uniform sample points on this path are then used as seeds to generate streamlines in the original flow field. Additional seeds are placed where a large gap between adjacent streamlines occurs. The number of short streamlines is significantly reduced as evenly spaced long streamlines spawned along the seeding paths can fill the topological regions very well. Several metrics for evaluating streamline placement quality are discussed and applied to our method as well as some other approaches. Compared to previous work in uniform streamline placement, our method is more effective in creating evenly spaced long streamlines and preserving topological features. It has the potential to provide both intuitive perception of important flow characteristics and detail reconstruction across visually pleasing streamlines.

In this paper, we present a new approach to detecting, locating and visualizing the local bifurcations originated from the changes of critical points in a 2D time-dependent vector field. Via precisely tracking the changes of all the critical points in a flow and pictorially displaying the evolution paths of these changes with a color model, our method can find and visualize all of this kind of bifurcations. This work extends Theisel's research on detecting a few local bifurcations based on the feature flow fields, and provides more complete information for probing into the structural instability in complex flow fields.