Turbulent flows play a critical role in many fields, yet our understanding of the fundamental physics of turbulence
remains in its infancy. One of the long term goals in turbulence research is to develop an improved understanding of the
dynamic evolution, interaction and organization of vortices in three-dimensional turbulent flow. However this task is
complicated by the lack of a clear, mathematically precise definition of what a vortex is. We believe that the design of
effective methods for vortex identification and segmentation in complicated turbulent flows can be facilitated by the
clear, detailed visual presentation of the multiple scalar and vector quantities potentially relevant to the feature
identification process. In this paper, we present several different methods aimed at facilitating the integrated
understanding of a variety of local measures extracted from 3D multivariate flow data, including quantities, directions,
and orientation. A key focus of our work is on the development of methods for illustrating the local relationships
between scalar and vector values important to the vortex identification process such as vorticity, swirl, and velocity,
along with their direction and magnitude. Our methods include the use of arrows and glyphs or 3D texture along with
different color coding strategies. We demonstrate our methods on a range of data including 3D turbulent boundary flow
data and time varying ring data. The variety of multi-variate visualization methods that we have developed has
succeeded in supporting fluids researchers in their efforts to gain deeper insights into their data.
The analysis of data that consists of multiple vector fields can be greatly facilitated by the simultaneous visualization
of the vector fields. An effective visualization must accurately reflect the key physical structures of the
fields in a way that does not allow for an unintended bias toward one distribution. While there are several effective
techniques to visualize a single vector field through equally spaced streamlines, applying these techniques
to individual vector fields and combining them in a single image yields undesirable artifacts. In this paper, we
present strategies for the effective visualization of two vector fields through the use of streamlines.
Proc. SPIE. 6057, Human Vision and Electronic Imaging XI
KEYWORDS: Statistical analysis, Human subjects, Visual process modeling, Visualization, Fourier transforms, Computer science, Human vision and color perception, Image classification, Computer graphics, Electronic imaging
This paper presents some insights into perceptual metrics for texture pattern categorization. An increasing number of researchers in the field of visualization are trying to exploit texture patterns to overcome the innate limitations of three dimensional color spaces. However, a comprehensive understanding of the most important features by which people group textures is essential for effective texture utilization in visualization. There have been a number of studies aiming at finding the perceptual dimensions of the texture. However, in order to use texture for multivariate visualization we need to first realize the circumstances under which each of these classification holds. In this paper we discuss the results of our three recent studies intended to gain greater insight into perceptual texture metrics. The first and second experiments investigate the role that orientation, scale and contrast play in characterizing a texture pattern. The third experiment is designed to understand the perceptual rules people utilize in arranging texture patterns based on the perceived directionality. Finally, in our last section we present our current effort in designing a computational method which orders the input textures based on directionality and explain its correlation with the human study.
Studies have shown that observers' judgment of surface orientation and curvature is affected by the presence of surface texture pattern. However, the question of designing a texture pattern that does not hide the surace information nor does convey a misrepresentation of the surface remains unsolved. The answer to this question has important potential imapct across a wide range of visualization application. Molecular modeling and radiation therapy are among the many fields that are in need of accurately visualizing their data and could benefit from such methods. Over the past several years we have carried out a series of experiments to investigate the impact of various texture pattern characteristics on shape perception. In this paper we report the result of our most recent study. The task in this study was adjusting surface attitude probes under three different texture conditions and a control condition in which no texture was present. We later compare the performances of the subjects. The three texture conditions were: a doubly oriented texture in which approximately evenly spaced lines followed both of the principal directions, a singly oriented texture in which lines followed only the first principal direction, and a singly oriented line integral convolution. Over a series of 200 trials (4 texture conditions, 10 surface probe locations * five repeated measures) a total of five naive participants were asked to adjust a circular probe. The probes were randomly located on an arbitrary curved surface and its perpendicular extension appeared to be oriented in the direction of the surface normal. An analysis of the results showed that the performance was best in the two directional texture condition. Performances were further decreased in one directional and no texture conditions (in that order). The paper is organized as follows. In Section 1 we briefly describe the motivation for our work. In Section 2 we describe our experimental methods, including a brief summary of the process of the stimuli preparation and a detailed presentation of statistic analysis of our experimental results. In Section 3 we discuss the implications of our findings and in the last section we will talk about our future plans.
If we could design the perfect texture pattern to apply to any smooth surface in order to enable observers to more accurately perceive the surface's shape in a static monocular image taken from an arbitrary generic viewpoint under standard lighting conditions, what would the characteristics of that texture pattern be? In order to gain insight into this question, our group has developed an efficient algorithm for synthesizing a high resolution texture pattern, derived from a provided 2D sample, over an arbitrary doubly curved surface in such a way that the orientation of the texture is constrained to follow a specified underlying vector field over the surface, at a per-pixel level, without evidence of seams or projective distortion artifacts. In this paper, we report the findings of a recent experiment in which we attempt to use this new texture synthesis method to assess the shape information carrying capacity of two different types of directional texture patterns (unidirectional and bi-directional) under three different orientation conditions (following the first principal direction, following a constant uniform direction, or swirling sinusoidally in the surface). In a four alternative forced choice task, we asked participants to identify the quadrant in which two B-spline surfaces, illuminated from different random directions and simultaneously and persistently displayed, differed in their shapes. We found, after all subjects had gained sufficient training in the task, that accuracy increased fairly consistently with increasing magnitude of surface shape disparity, but that the characteristics of this increase differed under the different texture orientation conditions. Subjects were able to more reliably perceive smaller shape differences when the surfaces were textured with a pattern whose orientation followed one of the principal directions than when the surfaces were textured with a pattern that either gradually swirled in the surface or followed a constant uniform direction in the tangent plane regardless of the surface shape characteristics. These findings appear to support our hypothesis that anisotropic textures aligned with the first principal direction may facilitate shape perception, for a generic view, by making more, reliable information about the extent of the surface curvature explicitly available to the observer than would be available if the texture pattern were oriented in any other way.
Perception of the 3D shape of a smoothly curving surface can be facilitated or impeded by the use of different surface texture patterns. In this paper we report the results of a series of experiments intended to provide insight into how to select or design an appropriate texture for shape representation in compute graphics. In these experiments, we examine the effect of the presence and direction of luminance texture pattern anisotropy on the accuracy of observers' judgements of 3D surface shape. Our stimuli consists of complicated, smoothly curving level surfaces from a typical volumetric dataset, across which we have generated four different texture patterns via 3D line integral convolution: one isotropic and three anisotropic, with the anisotropic patterns oriented across the surface either in a single uniform direction, in a coherently varying direction, or in the first principal direction at every surface point. Observers indicated shape judgements via manipulating an array of local probes so that their circular bases appeared to lie in the tangent plane to the surface at the probe's's center, and the perpendicular extensions appeared to point in the direction of the local surface normal. Stimuli were displayed as binocularly viewed flat images in the first trials, and in stereo during the second trials. Under flat viewing, performance was found to be better in the cases of the isotropic pattern and the anisotropic pattern that followed the first principal direction than in the cases of the other two anisotropic and principal direction patterns than for the other two. Our results are consistent with a hypothesis that texture pattern anisotropy impedes surface shape perception in the case that the direction of the anisotropy does not locally follow the direction of greatest normal curvature.