Illumination-aware group portrait compositor.

We present a novel compositing framework for full-length human figures that maintains their surface details and appends the localized nature of light and shadow, thereby synthesizing composite results with high visual coherence. The framework is extended from the compositing pipeline proposed in our previous study so that it deploys five stages for photometric information estimation, as well as for 3D reconstruction, global illumination simulation, lighting transfer, and compositing. Based on the interpretation that a sense of coexistence can be achieved through visual coherence, we demonstrate that the proposed framework functions properly as a group portrait compositor. The composite results that the proposed framework composed the images separately rendered 3D human models compared favorably with the results which rendered multiple avatars together. Based on this empirical evaluation, the proposed framework is expected as a new means of fostering a sense of coexistence in remote societies and of efficiently generating highly photorealistic cyberworlds.

TimeTubesX: A Query-Driven Visual Exploration of Observable, Photometric, and Polarimetric Behaviors of Blazars.

Blazars are celestial bodies of high interest to astronomers. In particular, through the analysis of photometric and polarimetric observations of blazars, astronomers aim to understand the physics of the blazar's relativistic jet. However, it is challenging to recognize correlations and time variations of the observed polarization, intensity, and color of the emitted light. In our prior study, we proposed TimeTubes to visualize a blazar dataset as a 3D volumetric tube. In this paper, we build primarily on the TimeTubes representation of blazar datasets to present a new visual analytics environment named TimeTubesX, into which we have integrated sophisticated feature and pattern detection techniques for effective location of observable and recurring time variation patterns in long-term, multi-dimensional datasets. Automatic feature extraction detects time intervals corresponding to well-known blazar behaviors. Dynamic visual querying allows users to search long-term observations for time intervals similar to a time interval of interest (query-by-example) or a sketch of temporal patterns (query-by-sketch). Users are also allowed to build up another visual query guided by the time interval of interest found in the previous process and refine the results. We demonstrate how TimeTubesX has been used successfully by domain experts for the detailed analysis of blazar datasets and report on the results.

Applying manifold learning to plotting approximate contour trees.

A contour tree is a powerful tool for delineating the topological evolution of isosurfaces of a single-valued function, and thus has been frequently used as a means of extracting features from volumes and their time-varying behaviors. Several sophisticated algorithms have been proposed for constructing contour trees while they often complicate the software implementation especially for higher-dimensional cases such as time-varying volumes. This paper presents a simple yet effective approach to plotting in 3D space, approximate contour trees from a set of scattered samples embedded in the high-dimensional space. Our main idea is to take advantage of manifold learning so that we can elongate the distribution of high-dimensional data samples to embed it into a low-dimensional space while respecting its local proximity of sample points. The contribution of this paper lies in the introduction of new distance metrics to manifold learning, which allows us to reformulate existing algorithms as a variant of currently available dimensionality reduction scheme. Efficient reduction of data sizes together with segmentation capability is also developed to equip our approach with a coarse-to-fine analysis even for large-scale datasets. Examples are provided to demonstrate that our proposed scheme can successfully traverse the features of volumes and their temporal behaviors through the constructed contour trees.

Visualization research is growing and expanding.

Visualization has become an increasingly active area of research because of its usefulness in a wide range of applications. This special issue features invited articles from PacificVis 2008 and highlights the state of the art in visualization algorithms, systems, and applications.