Visualizing 2-D vector fields using streamlines is one popular flow visualization technique. Standard streamline generation algorithms compute the density of streamlines across the domain, detect features, and employ customized rules to emphasize features. In this process, feature characterization and visual clarity are heavily considered. Simultaneously preserving the temporal coherence for time-varying vector fields, however remains a challenge. In this paper, we present a coherent and feature-aware streamline generation algorithm by employing a feature-guided streamline seeding technique and a coherent streamline placing scheme. For each frame, a feature map is first computed with critical points or the Finite-Time Lyapunov Exponent （FTLE） approach, and is used to initialize a set of seeds by leveraging the Poisson Disk distribution. These seeds are further optimized by using a deformation-driven moving mesh method. To preserve the temporal coherence, the streamlines generated from the seeds are individually checked subject to their correspondences to the ones in the previous frame. Subsequently, additional streamlines are sequentially inserted in low-density regions. We demonstrate our algorithm on both Computation Fluid Dynamics （CFD） and non-CFD datasets, and compare it with the recent literature
The node-link diagram is an intuitive way to depict a graph and present relationships between entities. Addressing the visual clutter induced by edge crossing and node-edge overlapping is a challenging task as the size of graph outgrows the visualization space. Many edge bundling methods are proposed to disclose high-level edge patterns. Though previous methods can successfully reveal the skeleton graph structure, the relation patterns at the individual node level can be overlooked. In addition, most edge bundling algorithms are computationally complex, which prevents them from scaling up for extremely large graphs. In this article, we extend SideKnot, an efficient edge bundling method to cluster and knot edges at the node side. Our proposed method is light, runs faster than most existing algorithms, and can reveal the relation patterns at the individual node level. Our results show that SideKnot can disclose a node＇s standing in the graph as well as the directional connection patterns to its peers.
Recently, sequential document visualization has attracted much attention for its superior capability in depicting the sequential semantic progression in a single document. However, existing methods commonly take abstractive visual forms such as texts, numbers, and glyphs, and require much user expertise for document exploration. In this paper we propose a sequential visualization to represent a single document with a two- dimensional picture-based storyline, which semantically enhances the comprehension of textual information. We introduce a new parametric modeling approach called the Hierarchical Parametric Histogram Curve （HPHC）, which encodes the statistical progression locally and adaptively. By transforming an HPHC into the two-dimensional space with a new locality-preserving embedding algorithm, we create a mapping from points along the curve to descriptive pictures and generate the visualization result. The new representation expresses the primary content with a graphical form, and allows for efficient multi-resolution and focus＋context exploration in a long document. Our approach compares favorably with previous work in that it is more intuitive and requires less user expertise. Informal evaluation shows that it is useful in quick document browsing, communication, and understanding, especially for people with low literacy skills.
