基于流线纹理合成的2D矢量场可视化

针对科学试验数据可视化问题,提出了一种2D矢量场可视化的方法－流线纹理合成方法,即通过将1D纹理映射到流线上,再利用流线纹理来合成可视化图象,因为移动1D纹理很容易形成矢量场动画。该方法是利用局部区域内流线的近似平行性,首先依据临界点来设定流线宽度,然后把流线绘制成多条平行流线,再分别将多条不同的1D纹理映射到流线上,从而能够加快计算。实际结果表明,由于流线纹理合成的纹理图象上清楚地显示了流线,因而反映了矢量场的方向信息,同时,该方法计算速度快,可以达到交互可视化的要求。     

基于分数阶微分的流线增强方法

为增强流线间强度对比,提高矢量场可视化效果,提出了一种基于分数阶微分滤波的流线增强方法.该方法通过构造分数阶微分算子,对卷积纹理在垂直矢量方向上进行分数阶微分滤波,增强了矢量线间的对比,改善了图像质量.实验结果表明,该方法能有效地增强矢量场流线可视化效果,为分析流场特征提供帮助.     

基于信息熵的流线质量评估和布局算法

基于流线的矢量场可视化是科学可视化的重要分支,在许多领域都有重要应用。但是现有的可视化方法很难有效评估流线对矢量场信息的反映程度。为了解决这一问题,提出了一种基于信息熵的矢量场种子点评估和布局算法。首先,基于信息熵进行初始布点捕捉矢量场中的主要特征,并利用模板将这些特征加强显示。然后,基于生成的流线恢复中间矢量场,通过恢复的矢量场和输入矢量场的条件熵值量化评估现有流线对矢量场的信息反映程度。根据条件熵和重要性添加新种子点。重新评估流线质量,反复迭代直到条件熵收敛。实验表明,该方法可以用较少的流线反映出矢量场中较多的信息。     

基于多频稀疏噪声的流场运动方向可视化算法

针对现有流场可视化算法存在的矢量场运动指向二义性以及缺乏对矢量场速率区分等问题进行了研究,提出了一种基于多频稀疏噪声纹理的改进线积分卷积算法,用来提高流场可视化后的信息表达效果。该方法首先根据矢量场速率量级的大小划分频率不同的噪声群,并合成适用于该向量场的特定多频噪声纹理;然后使用斜坡卷积核根据向量场流线对噪声纹理进行卷积;最后生成一幅同时具有矢量场运动方向和运动速率信息的彩色图像。实验结果表明,该方法可以增强用户对流场流量大小和矢量场方向的感知效果,在大规模风场数据可视化的验证实验上取得了良好的可视化效果。     

二维和三维矢量场的可视化

矢量场可视化用来帮助人们直观理解二维和三维矢量场。由于目前的矢量场可视化方法只能展示二维矢量场的方向而不能展示强度,并且对于三维矢量场可视化的效果不太令人满意,因此本文提出了用线积分卷积来实现二维平面矢量场可视化,以及三维空间矢量场可视化的方法。对于线积分卷积中的流线跟踪,利用扩展的Bresenham画线算法的思想,实现了二维平面和三维空间流线的跟踪,并且通过建立一个二维表和三维表来分别存储二维平面矢量场和三维空间矢量场中每个点在流线中的上一个点和下一点的位置,来避免传统流线跟踪方法的冗余计算,提高了效率。对于二维平面矢量场,把线积分卷积的结果和矢量的强度进行加权平均,从而利用输出图像的纹理和颜色,共同来表现矢量场的方向和强度。对于三维空间矢量场,利用体线积分卷积（Volume Line Integral Convolution, Volume LIC）的方法来得到输出体纹理,并且用光线投射的体渲染方法来展示三维空间矢量场。结果显示出本文的方法能够清晰直观的看到二维平面矢量场和三维空间矢量场。     

基于平面矢量场的可视化方法的研究

平面矢量场可视化是科学计算可视化的重要研究内容。矢量场以直观的图形图像显示场的运动,透过抽象数据有效洞察其内涵本质和变化规律,广泛应用于计算流体力学、航空动力学、大气物理和气象分析等领域。对平面矢量场进行可视化的方法有很多种,常见的有点图标法、流线法、线积分卷积法和拓扑分析法等。论文主要研究常见的可视化方法,分析它们的性能,并通过实际的例子,对具体的可视化结果图像进行比较。通过该研究,能够给人们在选择可视化方法时提供依据。     

基于平面矢量场的可视化方法的研究

平面矢量场可视化是科学计算可视化的重要研究内容.矢量场以直观的图形图像显示场的运动,透过抽象数据有效洞察其内涵本质和变化规律,广泛应用于计算流体力学、航空动力学、大气物理和气象分析等领域.对平面矢量场进行可视化的方法有很多种,常见的有点图标法、流线法、线积分卷积法和拓扑分析法等.论文主要研究常见的可视化方法,分析它们的性能,并通过实际的例子,对具体的可视化结果图像进行比较.通过该研究,能够给人们在选择可视化方法时提供依据.     

基于矢量完全信息熵的流线并行分布方法

[目的]流线可视化效果依赖于流线分布方法,其对矢量场数据的理解有着重要作用.现有的流线分布方法只考虑了矢量场的方向分量,忽略了另一重要的矢量长度信息.此外,为了提高计算效率,本文提出了一种基于矢量完全信息熵的并行流线分布方法.[方法]该方法考虑了矢量方向和长度分量,分块并行计算熵场并以此指导流线的放置.在放置过程中,采...     

改进的用于三维矢量场可视化的VolumeLIC算法

用于三维矢量场可视化的VolumeLIC算法比较耗时,而且生成的图像无法洞察场的内部信息,场的方向性也不明显。针对以上缺点,对原始VolumeLIC算法做了改进,它不同于以往的算法要计算整个矢量场,而是选取场中的部分点作为种子点,从这些点出发积分生成流线,对这些线上的点用VolumeLIC算法生成最终图像。实验结果表明,改进后的算法大幅提高了运算速度,并且空间方向感明显增强。     

基于CAD几何模型的曲面流线可视分析方法

针对在大规模矢量场中复杂几何曲面流线生成难的问题，设计实现了一种高精度的复杂曲面流线可视分析方法。首先，基于拓扑二维曲面结构进行求交计算，并引入高精度插值算法实现几何曲面矢量场并行抽取操作；然后，采用改进型龙格-库塔矢量场流线积分扩展技术进行积分计算，并引入自适应积分步长策略和流线-曲面求交加速结构，生成连续一致的曲面流线；最后，引入小球特征到可视化管线流程中，并集成于通用可视分析平台。实验结果表明，该方法能够高精度地生成连续一致的几何曲面流线，有效刻画复杂装置表面流场物理特性。     

Similarity-guided streamline placement with error evaluation

Most streamline generation algorithms either provide a particular density of streamlines across the domain or explicitly detect features, such as critical points, and follow customized rules to emphasize those features. However, the former generally includes many redundant streamlines, and the latter requires Boolean decisions on which points are features (and may thus suffer from robustness problems for real-world data). We take a new approach to adaptive streamline placement for steady vector fields in 2D and 3D. We define a metric for local similarity among streamlines and use this metric to grow streamlines from a dense set of candidate seed points. The metric considers not only Euclidean distance, but also a simple statistical measure of shape and directional similarity. Without explicit feature detection, our method produces streamlines that naturally accentuate regions of geometric interest. In conjunction with this method, we also propose a quantitative error metric for evaluating a streamline representation based on how well it preserves the information from the original vector field. This error metric reconstructs a vector field from points on the streamline representation and computes a difference of the reconstruction from the original vector field.     

基于Web的风场三维动态可视化设计与仿真

针对在Web端难以快速直观表达风场问题,提出一种基于Web的风场三维动态可视化设计与仿真方法。首先,为解决风场数据不易获取、处理不便问题,给出风场数据的获取、转换、应用流程。其次,设计使用Web Worker多线程技术并行计算生成流线的方法,减少风场流线寻迹生成时间,提高流线生成效率。然后,结合颜色映射技术,设计对流线进行Alpha颜色通道动态修改的方法,以表征风场动态流动。最后,扩展基于WebGL的可视化引擎Cesium进行风场三维可视化渲染,直观表达风场。结果表明,该方法能有效提高在Web端进行风场可视化仿真的效率,可为防风减灾提供支撑。     

An Illustrative Visualization Framework for 3D Vector Fields

Most 3D vector field visualization techniques suffer from the problem of visual clutter, and it remains a challenging task to effectively convey both directional and structural information of 3D vector fields. In this paper, we present a novel visualization framework that combines the advantages of clustering methods and illustrative rendering techniques to generate a concise and informative depiction of complex flow structures. Given a 3D vector field, we first generate a number of streamlines covering the important regions based on an entropy measurement. Then we decompose the streamlines into different groups based on a categorization of vector information, wherein the streamline pattern in each group is ensured to be coherent or nearly coherent. For each group, we select a set of representative streamlines and render them in an illustrative fashion to enhance depth cues and succinctly show local flow characteristics. The results demonstrate that our approach can generate a visualization that is relatively free of visual clutter while facilitating perception of salient information of complex vector fields.     

Evenly Spaced Streamlines for Surfaces: An Image-Based Approach

We introduce a novel, automatic streamline seeding algorithm for vector fields defined on surfaces in 3D space. The algorithm generates evenly spaced streamlines fast, simply and efficiently for any general surface-based vector field. It is general because it handles large, complex, unstructured, adaptive resolution grids with holes and discontinuities, does not require a parametrization, and can generate both sparse and dense representations of the flow. It is efficient because streamlines are only integrated for visible portions of the surface. It is simple because the image-based approach removes the need to perform streamline tracing on a triangular mesh, a process which is complicated at best. And it is fast because it makes effective, balanced use of both the CPU and the GPU. The key to the algorithm's speed, simplicity and efficiency is its image-based seeding strategy. We demonstrate our algorithm on complex, real-world simulation data sets from computational fluid dynamics and compare it with object-space streamline visualizations.     

Unsteady Flow Visualization by Animating Evenly-Spaced Streamlines

In recent years the work on vector field visualization has been concentrated on LIC-based methods. In this paper we propose an alternative solution for the visualization of unsteady flow fields. Our approach is based on the computation of temporal series of correlated images. While other methods are based on pathlines and try to correlate successive images at the pixel level, our approach consists in correlating instantaneous visualizations of the vector field at the streamline level. For each frame a feed forward algorithm computes a set of evenly-spaced streamlines as a function of the streamlines generated for the previous frame. This is achieved by establishing a correspondence between streamlines at successive time steps. A cyclical texture is mapped onto every streamline and textures of corresponding streamlines at different time steps are correlated together so that, during the animation, they move along the streamlines, giving the illusion that the flow is moving in the direction defined by the streamline. Our method gives full control on the image density so that we are able to produce smooth animations of arbitrary density, covering the field of representations from sparse, that is classical streamline-based images, to dense, that is texture-like images.