Han-Wei Shen

Bio: Han-Wei Shen is an academic researcher from Ohio State University. The author has contributed to research in topics: Visualization & Data visualization. The author has an hindex of 42, co-authored 227 publications receiving 5648 citations. Previous affiliations of Han-Wei Shen include Michigan Technological University & Ames Research Center.

A near optimal isosurface extraction algorithm using the span space

Abstract: Presents the "Near Optimal IsoSurface Extraction" (NOISE) algorithm for rapidly extracting isosurfaces from structured and unstructured grids. Using the span space, a new representation of the underlying domain, we develop an isosurface extraction algorithm with a worst case complexity of o(/spl radic/n+k) for the search phase, where n is the size of the data set and k is the number of cells intersected by the isosurface. The memory requirement is kept at O(n) while the preprocessing step is O(n log n). We utilize the span space representation as a tool for comparing isosurface extraction methods on structured and unstructured grids. We also present a fast triangulation scheme for generating and displaying unstructured tetrahedral grids.

View selection for volume rendering

Abstract: In a visualization of a three-dimensional dataset, the insights gained are dependent on what is occluded and what is not. Suggestion of interesting viewpoints can improve both the speed and efficiency of data understanding. This paper presents a view selection method designed for volume rendering. It can be used to find informative views for a given scene, or to find a minimal set of representative views which capture the entire scene. It becomes particularly useful when the visualization process is non-interactive - for example, when visualizing large datasets or time-varying sequences. We introduce a viewpoint "goodness" measure based on the formulation of entropy from information theory. The measure takes into account the transfer function, the data distribution and the visibility of the voxels. Combined with viewpoint properties like view-likelihood and view-stability, this technique can be used as a guide, which suggests "interesting" viewpoints for further exploration. Domain knowledge is incorporated into the algorithm via an importance transfer function or volume. This allows users to obtain view selection behaviors tailored to their specific situations. We generate a view space partitioning, and select one representative view for each partition. Together, this set of views encapsulates the "interesting" and distinct views of the data. Viewpoints in this set can be used as starting points for interactive exploration of the data, thus reducing the human effort in visualization. In non-interactive situations, such a set can be used as a representative visualization of the dataset from all directions.

A fast volume rendering algorithm for time-varying fields using a time-space partitioning (TSP) tree

Abstract: This paper presents a fast volume rendering algorithm for time-varying fields. We propose a new data structure, called Time-Space Partitioning (TSP) tree, that can effectively capture both the spatial and the temporal coherence from a time-varying field. Using the proposed data structure, the rendering speed is substantially improved. In addition, our data structure helps to maintain the memory access locality and to provide the sparse data traversal so that our algorithm becomes suitable for large-scale out-of-core applications. Finally, our algorithm allows flexible error control for both the temporal and the spatial coherence so that a trade-off between image quality and rendering speed is possible. We demonstrate the utility and speed of our algorithm with data from several time-varying CFD simulations. Our rendering algorithm can achieve substantial speedup while the storage space overhead for the TSP tree is kept at a minimum.

Isosurfacing in span space with utmost efficiency (ISSUE)

Abstract: We present efficient sequential and parallel algorithms for isosurface extraction. Based on the Span Space data representation, new data subdivision and searching methods are described. We also present a parallel implementation with an emphasis on load balancing. The performance of our sequential algorithm to locate the cell elements intersected by isosurfaces is faster than the Kd tree searching method originally used for the Span Space algorithm. The parallel algorithm can achieve high load balancing for massively parallel machines with distributed memory architectures.

Visualizing Changes of Hierarchical Data using Treemaps

Abstract: While the treemap is a popular method for visualizing hierarchical data, it is often difficult for users to track layout and attribute changes when the data evolve over time. When viewing the treemaps side by side or back and forth, there exist several problems that can prevent viewers from performing effective comparisons. Those problems include abrupt layout changes, a lack of prominent visual patterns to represent layouts, and a lack of direct contrast to highlight differences. In this paper, we present strategies to visualize changes of hierarchical data using treemaps. A new treemap layout algorithm is presented to reduce abrupt layout changes and produce consistent visual patterns. Techniques are proposed to effectively visualize the difference and contrast between two treemap snapshots in terms of the map items' colors, sizes, and positions. Experimental data show that our algorithm can achieve a good balance in maintaining a treemap's stability, continuity, readability, and average aspect ratio. A software tool is created to compare treemaps and generate the visualizations. User studies show that the users can better understand the changes in the hierarchy and layout, and more quickly notice the color and size differences using our method.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Knowledge Distillation: A Survey

Abstract: In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks. The great success of deep learning is mainly due to its scalability to encode large-scale data and to maneuver billions of model parameters. However, it is a challenge to deploy these cumbersome deep models on devices with limited resources, e.g., mobile phones and embedded devices, not only because of the high computational complexity but also the large storage requirements. To this end, a variety of model compression and acceleration techniques have been developed. As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. It has received rapid increasing attention from the community. This paper provides a comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison and applications. Furthermore, challenges in knowledge distillation are briefly reviewed and comments on future research are discussed and forwarded.