Haleh Hagh-Shenas

Bio: Haleh Hagh-Shenas is an academic researcher from University of Minnesota. The author has contributed to research in topics: Texture (geology) & Image texture. The author has an hindex of 9, co-authored 12 publications receiving 270 citations.

Weaving Versus Blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color.

Abstract: In many applications, it is important to understand the individual values of, and relationships between, multiple related scalar variables defined across a common domain. Several approaches have been proposed for representing data in these situations. In this paper we focus on strategies for the visualization of multivariate data that rely on color mixing. In particular, through a series of controlled observer experiments, we seek to establish a fundamental understanding of the information-carrying capacities of two alternative methods for encoding multivariate information using color: color blending and color weaving. We begin with a baseline experiment in which we assess participants' abilities to accurately read numerical data encoded in six different basic color scales defined in the L*a*b* color space. We then assess participants' abilities to read combinations of 2, 3, 4 and 6 different data values represented in a common region of the domain, encoded using either color blending or color weaving. In color blending a single mixed color is formed via linear combination of the individual values in L*a*b* space, and in color weaving the original individual colors are displayed side-by-side in a high frequency texture that fills the region. A third experiment was conducted to clarify some of the trends regarding the color contrast and its effect on the magnitude of the error that was observed in the second experiment. The results indicate that when the component colors are represented side-by-side in a high frequency texture, most participants' abilities to infer the values of individual components are significantly improved, relative to when the colors are blended. Participants' performance was significantly better with color weaving particularly when more than 2 colors were used, and even when the individual colors subtended only 3 minutes of visual angle in the texture. However, the information-carrying capacity of the color weaving approach has its limits. We found that participants' abilities to accurately interpret each of the individual components in a high frequency color texture typically falls off as the number of components increases from 4 to 6. We found no significant advantages, in either color blending or color weaving, to using color scales based on component hues thatare more widely separated in the L*a*b* color space. Furthermore, we found some indications that extra difficulties may arise when opponent hues are employed.

Conveying shape with texture: experimental investigations of texture's effects on shape categorization judgments

Abstract: We describe the results of two comprehensive controlled observer experiments intended to yield insight into the following question: If we could design the ideal texture pattern to apply to an arbitrary smoothly curving surface in order to enable its 3D shape to be most accurately and effectively perceived, what would the characteristics of that texture pattern be? We begin by reviewing the results of our initial study in this series, which were presented at the 2003 IEEE Symposium on Information Visualization, and offer an expanded analysis of those findings. We continue by presenting the results of a follow-on study in which we sought to more specifically investigate the separate and combined influences on shape perception of particular texture components, with the goal of obtaining a clearer view of their potential information carrying capacities. In each study, we investigated the observers' ability to identify the intrinsic shape category of a surface patch (elliptical, hyperbolic, cylindrical, or flat) and its extrinsic surface orientation (convex, concave, both, or neither). In our first study, we compared performance under eight different texture type conditions, plus two projection conditions (perspective or orthographic) and two viewing conditions (head-on or oblique). We found that: 1) shape perception was better facilitated, in general, by the bidirectional "principal direction grid" pattern than by any of the seven other patterns tested; 2) shape type classification accuracy remained high under the orthographic projection condition for some texture types when the viewpoint was oblique; 3) perspective projection was required for accurate surface orientation classification; and 4) shape classification accuracy was higher when the surface patches were oriented at a (generic) oblique angle to the line of sight than when they were oriented (in a nongeneric pose) to face the viewpoint straight on. In our second study, we compared performance under eight new texture type conditions, redesigned to facilitate gathering insight into the cumulative effects of specific individual directional components in a wider variety of multidirectional texture patterns. We found that shape classification accuracy was equivalently good under a variety of test patterns that included components following either the first or first and second principal directions, in addition to other directions, suggesting that a principal direction grid texture is not the only possible "best option" for enhancing shape representation.

Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color

Abstract: In this poster we present the results of two experiments in which we seek insight into the fundamental information-carrying capacities of two alternative methods for encoding multivariate information using color: color blending and color weaving [1]We begin with a baseline experiment, in which we assess participants' abilities to accurately read numerical data encoded via one of six different single-hued color scales defined via joint variations in saturation and luminance from 6 base colors (fig 1)To obtain the base colors, we selected six points evenly spaced about a circle of large constant saturation in the monitor gamut on a plane of constant luminance in the Lab color space (figure 2)From our first experiment, we were able to determine the average baseline level of accuracy that participants were able to achieve on the color matching task when asked to match a single colorIn our main experiment, we assessed participants' abilities to read combinations of 2, 3, 4 and 6 different data values simultaneously represented across a common region of the domain, encoded using either color blending, in which a single mixed color is formed via linear combination of the individual component colors, defined in Lab space (figure 3), or color weaving, in which the individual component colors appear side-by-side in a high frequency texture that fills the region (figure 4) Participants' viewing distance was restricted so that the blocks of constant color within this texture would subtend either 3 or 6 minutes of visual angleOur results indicate that performance was significantly better when the original color information was available via the high frequency texture than when the colors were blended (fig 5), and that this difference increased with the number of components

Weaving versus blending: a quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color

Abstract: In this poster we present the results of two experiments in which we seek insight into the fundamental information-carrying capacities of two alternative methods for encoding multivariate information using color: color blending and color weaving [1].We begin with a baseline experiment, in which we assess participants' abilities to accurately read numerical data encoded via one of six different single-hued color scales defined via joint variations in saturation and luminance from 6 base colors (fig 1). To obtain the base colors, we selected six points evenly spaced about a circle of large constant saturation in the monitor gamut on a plane of constant luminance in the Lab color space (figure 2). From our first experiment, we were able to determine the average baseline level of accuracy that participants were able to achieve on the color matching task when asked to match a single color.In our main experiment, we assessed participants' abilities to read combinations of 2, 3, 4 and 6 different data values simultaneously represented across a common region of the domain, encoded using either color blending, in which a single mixed color is formed via linear combination of the individual component colors, defined in Lab space (figure 3), or color weaving, in which the individual component colors appear side-by-side in a high frequency texture that fills the region (figure 4). Participants' viewing distance was restricted so that the blocks of constant color within this texture would subtend either 3 or 6 minutes of visual angle.Our results indicate that performance was significantly better when the original color information was available via the high frequency texture than when the colors were blended (fig 5), and that this difference increased with the number of components.

Showing Shape with Texture - Two Directions seem Better than One

Abstract: 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.

Visual comparison for information visualization

Abstract: Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools.

Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines

Abstract: We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.

Evaluating Information Visualizations

Abstract: Information visualization research is becoming more established, and as a result, it is becoming increasingly important that research in this field is validated. With the general increase in information visualization research there has also been an increase, albeit disproportionately small, in the amount of empirical work directly focused on information visualization. The purpose of this chapter is to increase awareness of empirical research in general, of its relationship to information visualization in particular; to emphasize its importance; and to encourage thoughtful application of a greater variety of evaluative research methodologies in information visualization.

Visualizing High-Dimensional Data: Advances in the Past Decade

Abstract: Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.

Visualizing High-Dimensional Data: Advances in the Past Decade.

Abstract: Massive simulations and arrays of sensing devices, in combination with increasing computing resources, have generated large, complex, high-dimensional datasets used to study phenomena across numerous fields of study. Visualization plays an important role in exploring such datasets. We provide a comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade. We aim at providing guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.