Showing papers by "David H. Laidlaw published in 2001"

CavePainting: a fully immersive 3D artistic medium and interactive experience

Abstract: CavePainting is an artistic medium that uses a 3D analog of 2D brush strokes to create 3D works of art in a fully immersive Cave environment. Physical props and gestures are used to provide an intuitive interface for artists who may not be familiar with virtual reality. The system is designed to take advantage of the 8 ft. x 8 ft. x 8 ft. space in which the artist works. CavePainting enables the artist to create a new type of art and provides a novel approach to viewing this art after it has been created. In this paper, we describe CavePainting’s 3D brush strokes, color pickers, artwork viewing mode, and interface. We also present several works of art created using the system along with feedback from artists. Artists are excited about this form of art and the gestural, full-body experience of creating it. CR Categories and Subject Descriptors: I.3.6 [Computer Graphics]: Methodology and Techniques - Interaction Techniques; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Virtual Reality; J.5 [Arts and Humanities]: Fine Arts Additional Key Words: 3D painting, 3D modeling, gestures, tangible user interface, Cave

An immersive virtual environment for DT-MRI volume visualization applications: a case study

Abstract: We describe a virtual reality environment for visualizing tensor-valued volumetric datasets acquired with diffusion tensor magnetic resonance imaging (DT-MRI). We have prototyped a virtual environment that displays geometric representations of the volumetric second-order diffusion tensor data and are developing interaction and visualization techniques for two application areas: studying changes in white-matter structures after gamma-knife capsulotomy and pre-operative planning for brain tumor surgery. Our feedback shows that compared to desktop displays, our system helps the user better interpret the large and complex geometric models, and facilitates communication among a group of users.

Assembling virtual pots from 3D measurements of their fragments

Abstract: A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. The overall approach is formulated and described and some detail is provided on the elements of the procedure. The end result is a representation suitable for comparisons, geometric feature extraction, visualization and digital archiving. Matching of fragments and aligning them geometrically is based on matching break-curves (curves on a pot surface separating fragments), estimated axes and profile curves for individual fragments and groups of matched fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. In our case, associated with subassemblies of fragments is a loglikelihood which is a sum of energy functions. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The addressed problem and solution can be considered as problems in "geometric learning" and in "perceptual grouping," where subgroups of pot fragments at a site location are to be assembled into individual virtual pots and other fragments are to be discarded as clutter.

Archaeological data visualization in VR: analysis of lamp finds at the great temple of petra, a case study

Abstract: Presents the results of an evaluation of the ARCHAVE (ARCHAeological Virtual Environment) system, an immersive virtual reality (VR) environment for archaeological research. ARCHAVE is implemented in a Cave. The evaluation studied researchers analyzing lamp and coin finds throughout the excavation trenches at the Petra Great Temple site in Jordan. Experienced archaeologists used our system to study excavation data, confirming existing hypotheses and postulating new theories they had not been able to discover without the system. ARCHAVE provided access to the excavation database, and researchers were able to examine the data in the context of a life-size representation of the present-day architectural ruins of the temple. They also had access to a miniature model for site-wide analysis. Because users quickly became comfortable with the interface, they concentrated their efforts on examining the data being retrieved and displayed. The immersive VR visualization of the recovered information gave them the opportunity to explore it in a new and dynamic way and, in several cases, enabled them to make discoveries that opened new lines of investigation about the excavation.

Quantitative comparative evaluation of 2D vector field visualization methods

Abstract: Presents results from a user study that compared six visualization methods for 2D vector data. Two methods used different distributions of short arrows, two used different distributions of integral curves, one used wedges located to suggest flow lines, and the final one was line-integral convolution (LIC). We defined three simple but representative tasks for users to perform using visualizations from each method: (1) locating all critical points in an image, (2) identifying critical point types, and (3) advecting a particle. The results show different strengths and weaknesses for each method. We found that users performed better with methods that: (1) showed the sign of vectors within the vector field, (2) visually represented integral curves, and (3) visually represented the locations of critical points. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand tradeoffs among methods. They may also be useful for evaluating 2D vectors on 2D surfaces embedded in 3D and for defining analogous tasks for 3D visualization methods.

Loose, artistic "textures" for visualization

Abstract: Through evolution, the human visual system has developed the ability to process natural textures. However, in addition to natural textures, humans also visually process man-made textures - some of the richest and most compelling of which are in works of art. Art goes beyond what perceptual psychologists understand about visual perception and there remain fundamental lessons that we can learn from art and art history that we can apply to our visualization problems. This article describes and illustrates some of the visualization lessons we have learned from studying art. I believe that these examples also illustrate some of the potential benefits of further study. While this approach is more open-ended than a perceptual psychology approach, both approaches are worthy of pursuit, and the potential benefits of using the less structured approach outweigh any risk of failure.

Elucidating Neural Structure in Diffusion Tensor MRI Volumes using Streamtubes and Streamsurfaces

Abstract: diffusion tensor MRI images. We distinguish between linear anisotropy and planar anisotropy and represent values within the two regimes using streamtubes and streamsurfaces, respectively. Streamtubes represent structures with primarily linear diffusion, typically fiber tracts; streamtube direction correlates with tract orientation. The cross-section shape and color of each streamtube are used to represent additional information for the diffusion tensor matrix at each point. Streamsurfaces represent structures in which diffusion is primarily planar. We also generate anatomical landmarks to identify the positions of prominent structures, such as eyes, skull surface, and ventricles. The final models are 2D surface geometries that can be imported into many interactive environments. In the literature, researchers have successfully designed visualization methods for 2D slices of diffusion tensor fields. These include ellipsoids2 as well as a normalized version of the ellipsoids and a painting-motivated method3. Directly extending these methods to volumes would not only be expensive but would also result in selfobscuring geometry. Two approaches have been explored for visualization of 3D second-order tensor fields. One uses volume rendering4, the other uses a geometric representation5. We extend the latter approach, originally applied to tensors related to fluid flow instead of diffusion, to visualize microstructural information in biological tissues. Methods We distinguish between structures exhibiting linear anisotropy and those exhibiting planar anisotropy. Streamtubes and streamsurfaces, respectively, represent these two types of diffusion. Streamtubes represent linear structures, where diffusion is much faster in one direction. The trajectory of each tube sweeps along the principal direction of diffusion, and the cross-section shape is an ellipse representing the diffusion rates in the directions perpendicular to the trajectory. We normalize the maximum radius of the ellipse to a constant value so that the size of the streamtube is predictable while its aspect ratio is preserved. The color of the streamtube shows how anisotropic the diffusion is. Streamsurfaces represent surface structures, where diffusion is faster within a plane than perpendicular to the plane. The surface we generate is an approximation of the integral surface perpendicular to the direction of slowest diffusion. Colors are mapped to the surfaces to show how anisotropic the diffusion is. Our algorithm begins by generating many streamtubes and streamsurfaces and then culls those down to a representative subset. Initially, every voxel with a linear or planar anisotropy value greater than some threshold has a representative streamtube or a streamsurface. The criteria for selecting the subset to display include the size of the geometry, the average anisotropy in the region containing the geometry, and the similarity of the geometries. Geometries with low scores on these criteria are discarded. A representative subset of geometries is kept and displayed in the final image. Results Figs 1 and 2 illustrate results of our method applied to data acquired from a human brain (data courtesy Dr. Susumu Mori, Johns Hopkins). Many gross features are readily apparent in the results; several are identified in the figures. There are 426 streamtubes in the final image after the culling process, compared to about 900,000 streamtubes initially generated. Fig 1. A front view of the human brain image using streamtubes (red), streamsurfaces (green) and anatomical landmarks (blue ventricles and wireframe brain surface). Anatomical features, including the corpus collosum and corona radiata, are clearly visible in the image.

Realism, expressionism, and abstraction: applying art techniques to visualization

Abstract: The visualization and computer graphics communities have recently become fascinated with the application of artistic techniques to three dimensional computer generated imagery. These are sometimes called non-photo realistic rendering techniques. This raises the key issue of when is it appropriate to apply realism, expressionism, and abstraction points of views to scientific and information visualization? What additional insights are gained from overlapping these approaches? The eye, the emotions, and the intellect all share in the process of creating and exploring visual art forms, including computer generated imagery. This panel examines a few non-photo realistic rendering approaches and highlights the impact these methods have on gaining insight from the resulting scientific and information visualizations.

Toward Application of Virtual Reality to Visualization of DT-MRI Volumes

Abstract: We describe a virtual reality application for visualizing tensor-valued volume data acquired with diffusion tensor magnetic resonance imaging (DT-MRI). We have prototyped a virtual environment that displays geometric representations of the volumetric 2nd-order diffusion tensor data and are developing interaction and visualization techniques for two application areas: studying changes in white-matter structures after gamma-knife capsulotomy and pre-operative planning for brain tumor surgery.