Analyzing structures of crowd mobility at city level is a challenging task due to the complex crowd mobility and dynamic changes generated by the social activities over time. These structures, defined as high-dimensional mobility structures (HMSs), contain spatiotemporal information and are simultaneously influenced by the geographical distributions and daily activities of citywide crowd. However, few work has been dedicated to depict and analyze these structures, mainly due to the lack of effective models. In this paper, we propose to model the crowd mobility as a dynamical system and characterize the irregular mobility data with a novel local coherence of sparse field (LCSF) algorithm. The proposed algorithm makes it possible to measure the separation behavior of trajectories in an irregular and sparse topology network. Detected HMS, referred as local separation measure of LCSF, divides the geographical urban areas into distinct functional regions over time. We design and implement a visual analytics system to facilitate situation-aware analysis of a huge amount of crowd mobility and their socialized behaviors. Case studies based on a real-world data set demonstrate the effectiveness of the proposed approach.

This paper presents an accurate parallel implementation of unsteady flow line integral convolution (UFLIC) for high-performance visualization of large time-varying flows. Our approach differs from previous implementations by using a novel value scattering+gathering mechanism to parallelize UFLIC and designing a pathline reuse strategy to reduce the computational cost of pathline integration. By exploiting the massive parallelism of modern graphical processing units (GPU), the proposed method allows for real-time dense visualization of unsteady flows with high spatial-temporal coherence.

Visual exploration is essential to the visualization and analysis of densely sampled 3D DTI fibers in biological specimens, due to the high geometric, spatial, and anatomical complexity of fiber tracts. Previous methods for DTI fiber visualization use zooming, color-mapping, selection, and abstraction to deliver the characteristics of the fibers. However, these schemes mainly focus on the optimization of visualization in the 3D space where cluttering and occlusion make grasping even a few thousand fibers difficult. This paper introduces a novel interaction method that augments the 3D visualization with a 2D representation containing a low-dimensional embedding of the DTI fibers. This embedding preserves the relationship between the fibers and removes the visual clutter that is inherent in 3D renderings of the fibers. This new interface allows the user to manipulate the DTI fibers as both 3D curves and 2D embedded points and easily compare or validate his or her results in both domains. The implementation of the framework is GPU based to achieve real-time interaction. The framework was applied to several tasks, and the results show that our method reduces the user's workload in recognizing 3D DTI fibers and permits quick and accurate DTI fiber selection.

Medical illustration has demonstrated its effectiveness to depict salient anatomical features while hiding the irrelevant details. Current solutions are ineffective for visualizing fibrous structures such as muscle, because typical datasets (CT or MRI) do not contain directional details. In this paper, we introduce a new muscle illustration approach that leverages diffusion tensor imaging (DTI) data and example-based texture synthesis techniques. Beginning with a volumetric diffusion tensor image, we reformulate it into a scalar field and an auxiliary guidance vector field to represent the structure and orientation of a muscle bundle. A muscle mask derived from the input diffusion tensor image is used to classify the muscle structure. The guidance vector field is further refined to remove noise and clarify structure. To simulate the internal appearance of the muscle, we propose a new two-dimensional example based solid texture synthesis algorithm that builds a solid texture constrained by the guidance vector field. Illustrating the constructed scalar field and solid texture efficiently highlights the global appearance of the muscle as well as the local shape and structure of the muscle fibers in an illustrative fashion. We have applied the proposed approach to five example datasets (four pig hearts and a pig leg), demonstrating plausible illustration and expressiveness.

Summary form only given. Traditionally, synthesizing the animation of floral blossom is accomplished mostly manually and hence a time-consuming and laborious task. In this paper we propose an interactive biology-based flower modeling and animating approach. We model the initial shape of the floral components including petal, pistil, stamen and pedicel, and two terminal status of floral blossom with a parameterized sketching interface. The subtle geometry of the whole flower is created according to the phyllotactic rules. We then extract a set of growth parameters for describing the floral blossom, and build a dynamic growth model to regulate the continuous morphing of floral components in the process of blossom. Finally, a sequence of naturally deforming flower models is generated with these parameters. Our initial experimental results demonstrate that our approach can efficiently generate visually pleasing simulation of floral blossom, which is consistent with biological rules.