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Research Areas My research focuses on data visualization, human-computer interaction and computer graphics. Some current research projects:
- Urban and Social Media Visualization
- Graph, Social Network, and High-Dimensional Data Visualization
- E-Learning

Research topics before 2009:
My research has advanced the state-of-the-art in intuitive volume visualization and clutter reduction in parallel coordinates and graphs, and also addressed some new problems from the real-world applications such as air pollution analysis.

  • Scientific Visualization

  • Information Visualization

  • Visual Analytics

  • Computer Graphics

 

Intuitive and effective volume visualization

Volume visualization is a major research topic in scientific visualization. Volume rendering, especially direct volume rendering, has been heavily investigated in the visualization field since the 1980s. Direct volume rendering is the most powerful and flexible volume visualization technique. However, it is still not widely used in practice. Thus, my research focuses on improving the usability of direct volume rendering, especially its intuitiveness, effectiveness, and efficiency. Our target users are doctors, scientists, and engineers who do not have the expertise on transfer function design.  The works we have done include:

  1. An editing framework for direct volume rendered images. We found that doctors are more interested in working on images, especially slices. Thus, we developed a novel method which allows end-users to directly edit volume rendered images.  The work was first presented as a poster at the IEEE Visualization 2006, which was nominated for the Best Poster Award. The extended version was published in the IEEE Transactions on Visualization and Computer Graphics 2007 [12].
  2. Automatic quality enhancements of direct volume rendered images. I believe that volume rendering engines are just like cameras. No matter how perfect the design is, there will always be some problems with the final rendered images. Thus, we developed a set of automatic enhancement tools for direct volume rendered images. The first tool is called quality enhancement. The work was published in Volume Graphics 2007 and featured in the cover of the proceedings.  The second tool is called perception-based transparency optimization.  The work has been accepted to the IEEE Visualization 2009 and published in the IEEE Transactions on Visualization and Computer Graphics [4].
  3. Relation-aware volume exploration pipeline. Spatial relation is one of the most important features in volumetric data. Direct volume rendering is a very effective way to reveal spatial relations between structures. However, the relation revealed in the direct volume rendered images may be ambiguous or even misleading. To address this problem, we proposed a relation-aware exploration pipeline for volumetric datasets.  This work was presented at the IEEE Visualization 2008 and published in the IEEE Transactions on Visualization and Computer Graphics [8].
  4. General effectiveness metrics for volume rendering. One major issue facing direct volume rendering is how to evaluate the effectiveness of the rendering results. Two traditional ways to validate visualization techniques are the expert reviews and user studies. However, these methods are mainly for experts to improve their systems. We believe that some built-in automatic effectiveness metrics will be very useful for end users. Similar to the built-in grammar and style checking in the Microsoft Word, our effectiveness metrics can automatically alert users on some ambiguous or misleading information in the rendered images or the visualization process. We propose four metrics, namely visibility metric, distinguishability metric, contour clarity metric, and coherence metric for the typical volume visualization systems. The preliminary result appeared in a poster at the IEEE Visualization 2007, which was nominated for the Best Poster Award. A journal version with an extensive user study is currently in preparation.

Impacts These four methods can dramatically improve the usability of volume visualization systems. They fit our vision that the future volume visualization system should be intuitive and safe to use, and the effectiveness of the visualization process will be automatically checked. Our research will facilitate a wider adoption of volume rendering in practice, which will benefit the end users of the visualization systems, such as physicians, engineers, and scientists.

 

Clutter reduction for parallel coordinates and graphs

Parallel coordinates and graphs are the most widely used information visualization techniques for multi-variate data and relational data analysis. However, visual clutter is a major problem. When there are too many data items, the display will become visually cluttered and the underlying patterns cannot be revealed. We proposed a novel scheme called visual clustering to reduce visual clutter and reveal underlying patterns for graphs and parallel coordinates. Our works include:

  1. Edge bundling for general graphs. Bundling edges together can dramatically improve the layout of graphs. However, previous edge bundling methods can only work for some special graphs. We proposed the first edge bundling framework for general graphs. Our method is original and based on control meshes. Our controllable and progressive edge clustering scheme was first published in Graph Drawing 2006, the top conference in the graph drawing area. An extended version with better mesh generation and advanced visualization techniques was presented at the IEEE Symposium on Information Visualization 2008 and published in the IEEE Transactions on Visualization and Computer Graphics 2008 [9]. An energy-based approach for edge bundling was presented in the IEEE Pacific Visualization Symposium 2008.
  2. Visual clustering and splatting for parallel coordinates. The traditional way to reduce visual clutter in parallel coordinates is brushing (i.e., filtering). Some pre-clustering in the data domain can also be used. However, some information will be lost. We proposed to use visual clustering to reduce visual clutter. Compared with brushing and pre-clustering, visual clustering can enhance and reveal interesting patterns in the data while preserving the context. Our process does not filter out any information. Instead, clusters are enhanced by visually bundling them together in the display. We developed two techniques, the energy-based clustering in the visual domain and the splatting-based clustering in the time domain. The first technique was presented at the EuroVis’08 [10] while the second approach was presented at the EuroVis’09 [7].  
  3. Combining scatter plots and parallel coordinates. Scatter plots can show the correlation between two variables while the distributions of data values in high dimensional space can only be fully revealed in parallel coordinates using polylines. They all have their advantages and disadvantages. Multi-dimensional scaling can use points projected onto a 2D plane to show the clusters in the high dimensional space. Thus, we developed an original framework to combine points and polylines via multi-dimensional scaling. This work has been accepted to the IEEE Information Visualization Symposium and will be published in the IEEE Transactions on Visualization and Computer Graphics [5].

 

Impacts Parallel coordinates and graphs are two cornerstone visual representations in information visualization. Our visual clustering scheme is novel and shows great potential. We will further pursue this line of research and investigate the differences between visual clustering in the display domain and traditional clustering in the data domain. Our research makes parallel coordinates and graphs more effective at revealing patterns in multi-variate data and relational data. We are currently applying our techniques in several visual data mining projects such as mining trajectory data.

 

Visual analytics and new problems from the real world applications

Visualization is a highly application-driven field and I have always paid special attention to some important problems from the real world. We have creatively applied and extended some established visualization techniques to solve these problems.

  1. Visual analysis of the air pollution problem in Hong Kong. The air quality problem in Hong Kong has aroused much attention recently. The Hong Kong government has established 18 observation stations, and the weather and air quality data are collected hourly. Some patterns of the air pollution problem have been revealed but some high level correlations are still elusive. Thus, we teamed up with the domain scientists from the Institute of Environments at HKUST to address this problem. We developed some novel visualization techniques such as the S-shape axis for parallel coordinates and integrated them into a comprehensive system for air pollution analysis. Our system is the first system developed especially for air pollution analysis. Our work has been widely recognized. The paper was presented at the IEEE Visualization 2007 and published in the IEEE Transactions on Visualization and Computer Graphics [11]. The basic system has won the HKICT Best Innovation Award in 2007. An RGC grant has been awarded to further improve the system.
  2. Interactive visual optimization and analysis for RFID benchmarking. RFID devices such as transportation cards and intelligent IDs have been widely used in the industry sector. Hong Kong has the world’s first major public transport system using the RFID technology. RFID has also been used in the logistic and supply chain management in Hong Kong. However, to successfully deploy such systems, benchmarking is required. HKUST has pioneered an α-gate system for RFID benchmarking. The system has been commercialized and has won an industrial award. However, the benchmarking results in a large amount of complex spatial temporal data. Manually analyzing these datasets will be very time consuming. Thus, we teamed up with a domain scientist to solve this problem. This work will be presented at the IEEE Visualization 2009 and published in the IEEE Transactions on Visualization and Computer Graphics [3].
  3. Visualizing the semantic structures of classic music works. My student Wing-Yi Chan took a music theory class during her last year of UG study. However, she found that it was difficult to understand the semantic structures in classical music works through the traditional ways such as essays and scores. She wanted to apply information visualization techniques to analyze the macro-relationships among layers, the micro-relationships of theme occurrences, and the macro-micro relationships between layer roles and theme variations. I closely worked with her and finally we developed a system which can reveal the beauty and sophistication of classical music works. The paper has been accepted to the IEEE Transactions on Visualization and Computer Graphics [2].
  4. Focus+Context zooming and information overlay in 3D urban environments. With the rapid development of 3D modeling and rendering technologies, it is now possible to model a whole city and then show them to users via Google Earth or Microsoft Bing Maps 3D. This opened doors to many applications, especially for tourists to virtually explore a city and plan their tours. A very common task that users often conduct in a 3D urban environment is to find a route from one building to another. We proposed a novel route zooming technique which creatively applied the seam carving algorithm and grid-based scaling to 3D urban environments. The method can provide seamless focus+context zooming to help users visualize a route with minimum distortions. It also provides occlusion-free information overlay such that some information (e.g., air pollution and other annotations) can be conveniently overlaid to the 3D urban environments. The work will be presented at IEEE Visualization 2009 and will be published in the IEEE Transactions on Visualization and Computer Graphics [1]. One image from the paper has been chosen for the front cover of the TVCG: Vis/InfoVis special journal issue.

 

Impacts Applications from the real world provide both challenges and opportunities for our field. Thus, I spend much effort and time talking with people in different fields and looking for promising applications. I am quite happy with the results. Our air pollution project with the Institute of Environment at HKUST has high potential because air pollution is a very serious problem in many regions and our system has been highly evaluated by the domain scientists. Our route zooming technique can also be used by Google Earth or Microsoft Bing Maps 3D to benefit a wider audience. Our music visualization system has received highly positive feedback from music students and experts. They believe it may change the way people learn classic music structures. From these applications, we have also identified some general research problems.  For example, we found that the macro/micro relations among the layers and themes in classical music works also exist in many other types of data such as news streams. We are currently investigating the visualization techniques to address this research problem.

 

Representative Publications:

(The names of my students are underlined.)

1.     Huamin Qu, Haomian Wang, Weiwei Cui, Yingcai Wu, Ming-Yuen Chan. “Focus+Context Route Zooming and Information Overlay in 3D Urban Environments”, IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009), vol. 15, no. 6, Nov.-Dec. 2009. (Cover Image)

2.     Wing-Yi Chan, Huamin Qu, Wai-Ho Mak. "Visualizing the Semantic Structure in Classical Music Works", IEEE Transactions on Visualization and Computer Graphics, accepted for publication.

3.     Yingcai Wu, Ka-Kei Chung, Huamin Qu, Xiaoru Yuan, Shing-Chi Cheung. “Interactive Visual Optimization and Analysis for RFID Benchmarking, IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009), vol. 15, no. 6, Nov.-Dec. 2009.

4.     Ming-Yuen Chan, Yingcai Wu, Wai-Ho Mak, Wei Chen, Huamin Qu. “perception-Based Transparency Optimization for Direct Volume Rendering”. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009), vol. 15, no. 6, Nov.-Dec. 2009.

5.     Xiaoru Yuan, Peihong Guo, He Xiao, Hong Zhou, Huamin Qu. “Scattering Points in Parallel Coordinates”, IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009), vol. 15, no. 6, Nov.-Dec. 2009.

6.     Wei Chen, Zi’ang Ding, Song Zhang, Anna M. Brandt, Stephen Correia, Huamin Qu, John A. Crow, David Tate, Zhicheng Yan, Qunsheng Peng. “A Novel Interface for Interactive Exploration of DTI Fibers”, IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009), vol. 15, no. 6, Nov.-Dec. 2009.

7.     Hong Zhou, Weiwei Cui, Huamin Qu, Yingcai Wu, Xiaoru Yuan, Wei Zhuo. “Splatting the Lines in Parallel Coordinates”, Computer Graphics Forum (Proceedings of EuroVis'09), vol. 28. no.3, pages 759 – 766, 2009. (Cover Image)

8.     Ming-Yuen Chan, Huamin Qu, Ka-Kei Chung, Wai-Ho Mak, and Yingcai Wu, "Relation-Aware Volume Exploration Pipeline", IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2008), vol. 14, no. 6, pages 1683-1690, Nov.-Dec. 2008.

9.     Weiwei Cui, Hong Zhou, Huamin Qu, Pak Chung Wong, and Xiaoming Li, "Geometry-Based Edge Clustering for Graph Visualization", IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2008), vol. 14, no. 6, pages 1277-1284, Nov.-Dec. 2008.  (Cover Image)

10.  Hong Zhou, Xiaoru Yuan, Huamin Qu, Weiwei Cui, Baoquan Chen. "Visual Clustering in Parallel Coordinates", Computer Graphics Forum (Proceedings of EuroVis'08), vol. 27, no. 3, 2008.

11.  Huamin Qu, Wing-Yi Chan, Anbang Xu, Kai-Lun Chung, Kai-Hon Lau, Ping Guo. "Visual Analysis of The Air Pollution Problem in Hong Kong", IEEE Transactions on Visualization and Computer Graphics (Proceedings of Visualization / Information Visualization 2007), vol. 13, no. 6, Nov.-Dec. 2007.

12.  Yingcai Wu, Huamin Qu. “Interactive Transfer Function Design Based on Editing Direct Volume Rendered Images”, IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No.5, pp. 1027—1040, 2007.

13.  Ming-Yuen Chan, Yingcai Wu, and Huamin Qu. "Quality Enhancement of Direct Volume Rendered Images", 6th IEEE/EG International Symposium on Volume Graphics (VG'07), pp. 25 – 32, 2007.   (Cover Image)

14.  Huamin Qu, Hong Zhou, Yingcai Wu. “Controllable and Progressive Edge Clustering for Large Networks”, 14th International Symposium on Graph Drawing (GD’06), pp. 399 – 404, Karlsruhe, Germany, September 18 - 20, 2006.

 

 

 

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