David L. Kao

A new line integral convolution algorithm for visualizing time-varying flow fields

New challenges on vector field visualization emerge as time dependent numerical simulations become ubiquitous in the field of computational fluid dynamics (CFD). To visualize data generated from these simulations, traditional techniques, such as displaying particle traces, can only reveal flow phenomena in preselected local regions and thus, are unable to track the evolution of global flow features over time. The paper presents an algorithm, called UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Our algorithm extends a texture synthesis technique, called Line Integral Convolution (LIC), by devising a new convolution algorithm that uses a time-accurate value scattering scheme to model the texture advection. In addition, our algorithm maintains the coherence of the flow animation by successively updating the convolution results over time. Furthermore, we propose a parallel UFLIC algorithm that can achieve high load balancing for multiprocessor computers with shared memory architecture. We demonstrate the effectiveness of our new algorithm by presenting image snapshots from several CFD case studies.

UFLIC: a line integral convolution algorithm for visualizing unsteady flows

The paper presents an algorithm, UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Using line integral convolution (LIC) as the underlying method, a new convolution algorithm is proposed that can effectively trace the flows global features over time. The new algorithm consists of a time-accurate value depositing scheme and a successive feedforward method. The value depositing scheme accurately models the flow advection, and the successive feedforward method maintains the coherence between animation frames. The new algorithm can produce time-accurate, highly coherent flow animations to highlight global features in unsteady flow fields. CFD scientists, for the first time, are able to visualize unsteady surface flows using the algorithm.

Visualizing spatial multivalue data

We introduce multivalue data as a new data type in the context of scientific visualization. While this data type has existed in other fields, the visualization community has largely ignored it. Formally, a multivalue datum is a collection of values about a single variable. Multivalue data sets can be defined for multiple dimensions. A spatial multivalue data set consists of a multivalue datum at each physical location in the domain. The time dimension is equally valid. This leads to spatio-temporal multivalue data sets where there is time varying, multidimensional data with a multivalue datum at each location and time. The spatial multivalue data type captures multiple instances of the same variable at each location in space. Visualizing spatial multivalue data sets is a new challenge.

Enhanced line integral convolution with flow feature detection

The line integral convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries where flow separation and re-attachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and re- attachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.

VISUALIZING DISTRIBUTIONS FROM MULTI-RETURN LIDAR DATA TO UNDERSTAND FOREST STRUCTURE

Abstract Spatially distributed probability density functions (pdfs) are becoming more relevant to Earth scientists and ecologists because of stochastic models and new sensors that provide numerous realizations or data points per unit area. One source of these data is from multi-return airborne lidar, a type of laser that records multiple returns for each pulse of light sent towards the ground. Data from multi-return lidar is a vital tool in helping us understand the structure of forest canopies over large extents. This paper presents visualization tools to allow scientists to rapidly explore, interpret and discover characteristic distributions within the entire spatial field. The major contribution of this work is a paradigm shift which allows ecologists to think of and analyse their data in terms of full distributions, not just summary statistics. The tools allow scientists to depart from traditional parametric statistical analyses and to associate multimodal distribution characteristics to forest structures. Information on the modality and shape of distributions, previously ignored, can now be visualized as well. Examples are given using data from High Island, southeast Alaska.

VISUALIZATION TECHNIQUES FOR SPATIAL PROBABILITY DENSITY FUNCTION DATA

Novel visualization methods are presented for spatial probability density function data. These are spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We use clustering as a means to reduce the information contained in these datasets; and present two different ways of interpreting and clustering the data. The clustering methods are used on two datasets, and the results are discussed with the help of visualization techniques designed for the spatial probability data.

An evaluation of using real-time volumetric display of 3D ultrasound data for intracardiac catheter manipulation tasks

The enthusiasm for novel, minimally invasive, catheter based intracardiac procedures has highlighted the need to provide accurate, realtime, anatomically based image guidance to decrease complications, improve precision, and decrease fluoroscopy time. The recent development of realtime 3D echocardiography creates the opportunity to greatly improve our ability to guide minimally invasive procedures (Ahmad, 2003). However, the need to present 3D data on a 2D display decreases the utility of 3D echocardiography because echocardiographers cannot readily appreciate 3D perspective on a 2D display without ongoing image manipulation. We evaluated the use of a novel strategy of presenting the data in a true 3D volumetric display, Perspecta Spatial 3D System (Actuality Systems, Inc., Burlington, MA). Two experienced echocardiographers performed the task of identifying the targeted location of a catheter within 6 different phantoms using four display methods. Echocardiographic images were obtained with a SONOS 7500 (Philips Medical Systems, Inc., Andover, MA). Completion of the task was significantly faster with the Perspecta display with no loss in accuracy. Echocardiography in 3D significantly improves the ability of echocardiography for guidance of catheter based procedures. Further improvement is achieved by using a true 3D volumetric display, which allows for more intuitive assessment of the spatial relationships of catheters in three-dimensional space compared with conventional 2D visualization modalities.

The uncertainty visualization problem in remote sensing analysis

Remote sensing analyses usually result in maps of discrete or continuous variables. Ideally, each value in such a map should be accompanied by an uncertainty description, that is, a quantitative statement about the probability of error. A full description of uncertainty at each pixel is usefully represented using a probability distribution. Such a probability distribution may be based on an understanding of potential errors in position, spatial support (the area measured by the sensors field of view), model parameters, the model structure, and the input variables to the model. Visualizing uncertainty in the products of remote sensing analysis presents the challenge that at least four dimensions are required. These are the spatial dimensions (x and y), the dimension of the variable being mapped and finally the probability dimension. Current visualization tools and techniques do not support these data sets directly. Even animation, a logical choice to represent other four dimensional problems, is not fully satisfactory for probability distribution data sets. We have first addressed the problem of visualizing uncertainty by creating interactive maps of first, second and third order statistics summarizing the distributions. Next, we have experimented with shaded surface rendering of distributions from a user-selectable profile (row or column) in the image. We demonstrate these methods using a data set generated by a geostatistical conditional simulation algorithm and a single band image and we discuss the future promise of visualizing all four dimensions at once.

Numerical surface flow visualization

Surface oil flow is an experimental flow visualization technique that depicts the surface flow pattern near the body of the model. Traditionally, a particle tracking technique that generates streamlines near the model body is used to depict surface flows in CFD flow simulations. In this paper, we compared surface flows represented by streamlines with those represented by a texture technique known as Line Integral Convolution (LIC). We found that streamlines used to depict surface flows are discontinuous in general and the quality of the surface flow pattern is highly dependent on the placement of the streamlines. Whereas, the LIC technique clearly depicts surface flows that closely resemble surface oil flows. We also found that surface flows near regions of vortex structures and saddle points are not best shown using the streamline technique compared to the LIC technique. For unsteady flow simulations, we compared streaklines with a new texture synthesis technique called Unsteady Flow Line Integral Convolution (UFLIC) that we have recently developed. UFLIC accurately reveals the dynamic behavior of unsteady surface flows during animation.

iGraph: a graph-based technique for visual analytics of image and text collections

In our daily lives, images and texts are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image and text collections. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords, but also supports detailed comparison for understanding and intuitive guidance for navigation. For performance speedup, multiple GPUs and CPUs are utilized for processing and visualization in parallel. We experiment with two image and text collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.