Haleh Hagh-Shenas

Conveying 3D shape with texture: recent advances and experimental findings

If we could design the perfect texture pattern to apply to any smooth surface in order to enable observers to more accurately perceive the surfaces 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.

Visualizing Motion Data in Virtual Reality: Understanding the Roles of Animation, Interaction, and Static Presentation

We present a study of interactive virtual reality visualizations of scientific motions as found in biomechanics experiments. Our approach is threefold. First, we define a taxonomy of motion visualizations organized by the method (animation, interaction, or static presentation) used to depict both the spatial and temporal dimensions of the data. Second, we design and implement a set of eight example visualizations suggested by the taxonomy and evaluate their utility in a quantitative user study. Third, together with biomechanics collaborators, we conduct a qualitative evaluation of the eight example visualizations applied to a current study of human spinal kinematics. Results suggest that visualizations in this style that use interactive control for the time dimension of the data are preferable to others. Within this category, quantitative results support the utility of both animated and interactive depictions for space; however, qualitative feedback suggest that animated depictions for space should be avoided in biomechanics applications.

A user study to understand motion visualization in virtual reality

Studies of motion are fundamental to science. For centuries, pictures of motion have factored importantly in making scientific discoveries possible. Today, there is perhaps no tool more powerful than interactive virtual reality (VR) for conveying complex space-time data to scientists, doctors, and others; however, relatively little is known about how to design virtual environments in order to best facilitate these analyses. In designing virtual environments for presenting scientific motion data (e.g., 4D data captured via medical imaging or motion tracking) our intuition is most often to “reanimate” these data in VR, displaying moving virtual bones and other 3D structures in virtual space as if the viewer were watching the data being collected in a biomechanics lab. However, recent research in other contexts suggests that although animated displays are effective for presenting known trends, static displays are more effective for data analysis.

Shape categorization from texture

Principal direction Swirly direction Uniform direction Experiment 2: Discrimination of subtle surface shape differences, measured in a four alternative forced choice task, is possible at lower thresholds, with line and grid-like patterns, when the texture coordinate system is locally aligned with the principal directions than when it is aligned with a constant uniform direction or with a direction that turns in the surface.

A closer look at texture metrics for visualization

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.