时间:2017年8月19日星期六
地点:浙江大学紫金港校区蒙民伟楼CAD&CG国家重点实验室402室
Morning:
9:00 – 9:40 Opening & Keynote by Leila
Topology-based Methods for spatial data analysis and visualization
9:40 – 10:40 Panel: How to increase the visibility of young Chinese researchers?
10:40 – 11:00 Tea Break.
11:00-12:15 Graphics&VIS Talks:
Ligang Liu:Smooth Assembled Mappings for Large-Scale Real Walking in VR
Lei Shi: Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation
Yingcai Wu: Spatio-temporal Data Visualization
Bin Wang: Gauss Surface Reconstruction
Liang Wan: Panoramic Image Processing
Afternoon:
2:00 – 2:40 Keynote by Hanan
Reading News with Maps by Exploiting Spatial Synonyms
2:40 – 3:40 VIS&Graphics Talks:
Shixia Liu: Interpretable Machine Learning with Interactive Visualization
Yunhai Wang: Task-driven Automated Data Visualization
Jin Huang: Hex Remeshing
Dongming Yan: Isotropic Surface Remeshing without Large and Small Angle
3:40 – 4:00 Tea Break.
4:00-5:00 Demo Session
Leila De Floriani
University of Maryland at College Park
Keynote title: Topology-based Methods for spatial data analysis and visualization
Abstract:
Analyzing large spatial data sets requires efficient data management techniques, powerful analysis algorithms and visualization methods which allow domain experts to effectively interact with data. Advanced tools from combinatorial topology, such as persistent homology and Morse theory, provide a theoretically well-justified, and parameter-free way to extract the complex intrinsic structures of data in a very concise format, but are computationally intensive for current large-size data sets. In this talk I will present our work in topological analysis of big spatial data, specifically point data equipped with one or more function values: scalar fields (terrains, 2D or 3D images, unstructured volume data sets, etc.), and multifields, which are collections of fields with different modalities (e.g., pressure and density in physical simulations). I will focus on topology-based visual analytics tools to support interactive data analysis, and discuss scalability issues.
Biography:
Leila De Floriani is a Professor at the University of Maryland at College Park. She has been Professor of Computer Science at the University of Genova in Italy since 1990, where she founded the Geometry and Computer Graphics group, and where she has been the Director of the PhD Program in Computer Science for eight years. She has also held positions at the Italian National Research Council, at the University of Nebraska, at Rensselaer Polytechnic Institute, and at the University of Maryland.
Leila De Floriani has written over 280 publications in the fields of geometric and solid modeling, shape analysis, scientific data visualization, terrain modeling and geospatial data processing, which have appeared in major international journals and conferences. The main focus of her current research is in topological data analysis, geometric and topological data representations, geometric algorithms for scientific visualization and topology-based visual analytics.
Leila De Floriani is the Editor-in-Chief of the IEEE Transactions on Visualization and Computer Graphics. She is currently an Associate Editor of Graphical Models and of the ACM Transactions on Spatial Algorithms and Systems, and of GeoInformatica. She served on more then 150 program committees of the major international conferences in geometric modeling, computer graphics, visualization, and geospatial data processing. She is a Fellow of the IEEE, a Fellow of the International Association for Pattern Recognition (IAPR), a Member of the ACM and of the Eurographics Association. She is serving as a Member of the IEEE Computer Society Board of Governors for the years 2017-2019.
Hanan Samet
University of Maryland at College Park
Keynote title: Reading News with Maps by Exploiting Spatial Synonyms
Abstract:
NewsStand is an example application of a general framework to enable people to search for information using a map query interface, where the information results from monitoring the output of over 10,000 RSS news sources and is available for retrieval within minutes of publication. The advantage of doing so is that a map, coupled with an ability to vary the zoom level at which it is viewed, provides an inherent granularity to the search process that facilitates an approximate search. This distinguishes it from today's prevalent keyword-based conventional search methods that provide a very limited facility for approximate searches and which are realized primarily by permitting a match via use of a subset of the keywords. However, it is often the case that users do not have a firm grasp of which keyword to use, and thus would welcome the search to also take synonyms into account. For queries to spatially-referenced data, the map query interface is a step in this direction as the act of pointing at a location (e.g., by the appropriate positioning of a pointing device) and making the interpretation of the precision of this positioning specification dependent on the zoom level is equivalent to permitting the use of spatial synonyms (i.e., letting spatial proximity play a role rather than only seeking an exact match of a query string). Of course, this is all predicated on the use of a textual specification of locations rather than a geometric one, which means that one must deal with the potential for ambiguity.
The issues that arise in the design of a system like NewsStand, including the identification of words that correspond to geographic locations, are discussed, and examples are provided of its utility. More details can be found in the video at http://vimeo.com/106352925 which accompanies the “cover article'' of the October 2014 issue of the Communications of the ACM about NewsStand at http://tinyurl.com/newsstand-cacm
or a cached version at http://www.cs.umd.edu/~hjs/pubs/cacm-newsstand.pdf.
Biography:
Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies. He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications, geographic information systems, computer graphics, computer vision, image processing, games, robotics, and search. He received the B.S. degree in engineering from UCLA, and the M.S. Degree in operations research and the M.S. and Ph.D. degrees in computer science from Stanford University. His doctoral dissertation dealt with proving the correctness of translations of LISP programs which was the first work in translation validation and the related concept of proof-carrying code. He is the author of the recent book "Foundations of Multidimensional and Metric Data Structures" (http://www.cs.umd.edu/~hjs/multidimensional-book-flyer.pdf) published by Morgan-Kaufmann, an imprint of Elsevier, in 2006, an award winner in the 2006 best book in Computer and Information Science competition of the Professional and Scholarly Publishers (PSP) Group of the American Publishers Association (AAP), and of the first two books on spatial data structures "Design and Analysis of Spatial Data Structures", and "Applications of Spatial Data Structures: Computer Graphics, Image Processing, and GIS", both published by Addison-Wesley in 1990. He is the Founding Editor-In-Chief of the ACM Transactions on Spatial Algorithms and Systems (TSAS), the founding chair of ACM SIGSPATIAL, a recipient of a Science Foundation of Ireland (SFI) Walton Visitor Award at the Centre for Geocomputation at the National University of Ireland at Maynooth (NUIM), 2009 UCGIS Research Award, 2010 CMPS Board of Visitors Award at the University of Maryland, 2011 ACM Paris Kanellakis Theory and Practice Award, 2014 IEEE Computer Society Wallace McDowell Award, and a Fellow of the ACM, IEEE, AAAS, IAPR (International Association for Pattern Recognition), and UCGIS (University Consortium for Geographic Science). He received best paper awards in the 2007 Computers & Graphics Journal, the 2008 ACM SIGMOD and SIGSPATIAL ACMGIS Conferences, the 2012 SIGSPATIAL MobiGIS Workshop, and the 2013 SIGSPATIAL GIR Workshop, as well as a best demo paper award at the 2011 and 2016 SIGSPATIAL ACMGIS Conferences. His paper at the 2009 IEEE International Conference on Data Engineering (ICDE) was selected as one of the best papers for publication in the IEEE Transactions on Knowledge and Data Engineering. He was elected to the ACM Council as the Capitol Region Representative for the term 1989-1991, and was an ACM Distinguished Speaker for the term 2008-2015.
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Shixia Liu
Tsinghua University
Title: Interpretable Machine Learning with Interactive Visualization
Short bio: Shixia Liu is an associate professor at Tsinghua University. Her research interests include visual text analytics, visual social analytics, visual behavior analytics, graph visualization, and tree visualization. Before joining Tsinghua University, she worked as a lead researcher at Microsoft Research Asia and a research staff member at IBM China Research Lab. Shixia is one of the Papers Co-Chairs of IEEE VAST 2016 and 2017. She is an associate of IEEE Transactions on Visualization and Computer Graphics and is on the editorial board of Information Visualization. She was the guest editor of ACM Transactions on Intelligent Systems and Technology and Tsinghua Science and Technology. She was the program co-chair of PacifcVis 2014 and VINCI 2012. Shixia was in the Steering Committee of VINCI 2013. She is on the organizing committee of IEEE VIS 2015 and 2014. She is/was in the Program Committee for CHI 2018, InfoVis 2015, 2014, VAST 2015, 2014, KDD 2015, 2014, 2013, ACM Multimedia 2009, SDM 2008, ACM IUI 2011, 2009, PacificVis 2008, 2009, 2010, 2011, PAKDD 2013, VISAPP 2012, 2011, VINCI 2011.
Yingcai Wu
Zhejiang University
Title: Spatio-temporal data visualization
Short bio:
Yingcai Wu is a National Youth-1000 scholar and a ZJU100 Young Professor at the State Key Lab of CAD & CG, Zhejiang University. He obtained his Ph.D. degree in Computer Science from the Hong Kong University of Science and Technology (HKUST). Prior to his current position, Yingcai Wu was a researcher in the Microsoft Research Asia, Beijing, China from 2012 to 2015, and a postdoctoral researcher at the University of California, Davis from 2010 to 2012.
His main research interests are in visual analytics, information visualization, and human-computer interaction, with focuses on urban computing, social media analysis, text visualization, and behavior analysis. He has published more than 50 refereed papers, including 21 IEEE TVCG papers. His three papers have been awarded Honorable Mention at IEEE VIS (SciVis) 2009, IEEE VIS (VAST) 2014, and IEEE PacificVis 2016. For more information, visit www.ycwu.org
Yingcai was one of the Papers Co-Chairs of IEEE Pacific Visualization 2017, ChinaVis 2016, ChinaVis 2017, and VINCI 2014. He is/was also the guest editor of IEEE Transactions on Visualization and Computer Graphics (TVCG), ACM Transactions on Intelligent Systems and Technology (TIST), and IEEE Transactions on Multimedia. He was in the Program Committee for InfoVis 2017, 2016, 2015, SciVis 2017, 2016, 2015, 2014, EuroVis 2015, 2016, 2017, PacificVis 2011, 2012, 2013, 2015, 2016.
Yunhai Wang
Shandong University
Title: Task-driven Automated Data Visualization
Abstract:
By providing visual representation of data, visualization can help people carry out some tasks more effectively. Given a data set, however, there are have too many different visualization techniques, where each technique has many parameters to be tweaked. We are asking if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. We have developed five new techniques to achieve this goal for specific data sets: perception-driven dimensionality reduction, the selection of line chart or scatter plot for time-series data, a framework for aspect ratio selection, consistency-preserving word cloud editing and constrained graph exploration.
Short bio: Yunhai Wang is an associate professor in School of Computer Science and Technology at Shandong University. His interests include scientific visualization, information visualization and 3d shape analysis, focusing specifically on automated data visualization.
Lei Shi
the State Key Laboratory of Computer Science
Institute of Software, Chinese Academy of Sciences
Title: Blockwise Human Brain Network Visual Comparison Using NodeTrix Representation
Abstract: Visually comparing human brain networks from multiple population groups serves as an important task in the field of brain connectomics. The commonly used brain network representation, consisting of nodes and edges, may not be able to reveal the most compelling network differences when the reconstructed networks are dense and homogeneous. In this paper, we leveraged the block information on the Region Of Interest (ROI) based brain networks and studied the problem of blockwise brain network visual comparison. An integrated visual analytics framework was proposed. In the first stage, a two-level ROI block hierarchy was detected by optimizing the anatomical structure and the predictive comparison performance simultaneously. In the second stage, the NodeTrix representation was adopted and customized to visualize the brain network with block information. We conducted controlled user experiments and case studies to evaluate our proposed solution. Results indicated that our visual analytics method outperformed the commonly used node-link graph and adjacency matrix design in the blockwise network comparison tasks. We have shown compelling findings from two real-world brain network data sets, which are consistent with the prior connectomics studies.
Short bio: Lei Shi is an associate professor in the State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences. Before that, he was a research staff member and research manager at IBM Research – China. He holds B.S. (2003), M.S. (2006) and Ph.D. (2008) degrees from Department of Computer Science and Technology, Tsinghua University. His current research interests are Visual Analytics and Data Mining, with more than 70 papers published in top-tier venues, such as IEEE TVCG, TKDE, TC, VIS, ICDE, ICDM, Infocom, ACM Sigcomm and CSCW. He is the recipient of IBM Research Division Award on "Visual Analytics" and the IEEE VAST Challenge Award twice in 2010 and 2012. He has organized several workshops on combining visual analytics and data mining, in ICDM, CIKM, etc, and served on the program committees of many related conferences such as KDD, Graph Drawing, etc. He is an IEEE senior member.
Jin Huang
Zhejiang University
Title: Hex Remeshing
Abstract: Hex mesh has important applications in graphics and mechanics. In this talk, we will show how to address some key challenges in hex remeshing, the “holy grail” problem in remeshing, and discuss the possible future works.
Short bio: Prof. Jin Huang got the Ph.D degree at 2007 from Zhejiang university. He visited Caltech from 2012 to 2014, and was awarded Excellent Ph.D thesis of China Computer Federation at 2008 and the NSFC Excellent Young Scholars Program at 2015. His research interests are mainly in the areas of geometry processing and physically based simulation. He has served as PC of SIGGRAPH Asia, SGP, SCA etc. conference, associated editor of CAGD and program co-chair of Geometric Modeling and Processing (GMP) 2016.
Ligang Liu
University of Science and Technology of China
Title: Smooth Assembled Mappings for Large-Scale Real Walking
Short bio: Ligang Liu is a professor at the School of Mathematical Sciences, University of Science and Technology of China. He received his B.Sc. (1996) and his Ph.D. (2001) from Zhejiang University, China. Between 2001 and 2004, he worked at Microsoft Research Asia. Then he worked at Zhejiang University during 2004 and 2012. He paid an academic visit to Harvard University during 2009 and 2011. His research interests include digital geometric processing, computer graphics, and image processing. He serves as the associated editors for journals of IEEE Transactions on Visualization and Computer Graphics, IEEE Computer Graphics and Applications, Computer Graphics Forum, Computer Aided Geometric Design, and The Visual Computer. He served as the conference co-chair of GMP 2017 and the program co-chairs of GMP 2018, CAD/Graphics 2017, CVM 2016, SGP 2015, and SPM 2014. His research works could be found at his research website: http://staff.ustc.edu.cn/~lgliu
Bin Wang
Tsinghua University
Title:Gauss Surface Reconstruction
Abstract: In this paper, we introduce a surface reconstruction method that can perform gracefully with non-uniformly-distributed, noisy, and even sparse data. We reconstruct the surface by estimating an implicit function and then obtain a triangle mesh by extracting an iso-surface from it. Our implicit function takes advantage of both the indicator function and the signed distance function. It is dominated by the indicator function at the regions away from the surface and approximates (up to scaling) the signed distance function near the surface. On one hand, it is well defined over the entire space so that the extracted iso-surface must lie near the underlying true surface and is free of spurious sheets. On the other hand, thanks to the nice properties of the signed distance function, a smooth iso-surface can be extracted using the approach of marching cubes with simple linear interpolations. More importantly, our implicit function can be estimated directly from an explicit integral formula without solving any linear system. This direct approach leads to a simple, accurate and robust reconstruction method, which can be paralleled with little overhead. We call our reconstruction method Gauss surface reconstruction. We apply our method to both synthetic and real-world scanned data and demonstrate the accuracy, robustness and efficiency of our method. The performance of Gauss surface reconstruction is also compared with that of several state-of-the-art methods.
Short bio: Bin Wang is currently an associate professor at School of Software, Tsinghua University, China. He received his B.Sc. degree in Chemistry in 1999, and his Ph.D. in Computer Science from Tsinghua University in 2005. He was a research assistant at Department of Computer Science, Hong Kong University, and had postdoctoral research training at ISA/ALICE Research Group, INRIA-LORIA, France. His research interests include geometry processing, and image and video processing.
Dong-Ming Yan
National Laboratory of Pattern Recognition of the Institute of Automation
Chinese Academy of Sciences
Title: Isotropic Surface Remeshing without Large and Small Angles
Abstract: We introduce a novel algorithm for isotropic remeshing which progressively eliminates obtuse triangles and improves small angles. The main novelty of the proposed approach are a simple vertex insertion scheme that facilitates the removal of obtuse angles, and a vertex removal operation that improves the distribution of small angles. Combined with other standard local mesh operators, e.g., connectivity optimization and local tangential smoothing, our algorithm is able to remesh a low quality mesh surface efficiently. Our approach can be used as a post-processing step following other remeshing approaches or applied directly. Our method has a similar computational efficiency compared to the fastest approach available, i.e., real-time adaptive remeshing. Compared with state-of-the-art approaches, our method consistently generates better results evaluated by different metrics.
Short bio:Dong-Ming Yan received his PhD degree from Hong Kong University in 2010, and his Bachelor's and Master's degrees from Tsinghua University in 2002 and 2005, respectively. He is an associate professor at the National Laboratory of Pattern Recognition of the Institute of Automation, Chinese Academy of Sciences. His research interests include computer graphics, geometric processing and visualization.
Liang Wan
Tianjin University
Title: Panoramic Image Processing
Abstract: Panorama is an effective and convenient way to represent large filed-of-view environment. With the rapid development of image stitching and panorama camera technologies, panoramas have been becoming prevalent. A spherical panorama owns characteristics differing it from normal 2D images. It covers 360-degree environment and defined on the sphere domain.
We have exploited panoramic image processing in depth from several aspects. To be specific, we have focused on the virtual navigation between panoramas in real-time (TMM 2013), addressed the problem of rapid and robust feature detection and matching for panoramas (IJCV 2015), and developed a spherical superpixel segmentation method (ICME 2016, TMM with major revision).
Short bio: Liang Wan is an Associate Professor in the School of Computer Software, Tianjin University, China. She received her Ph.D. degree in Computer Science and Engineering from The Chinese University of Hong Kong (CUHK) in 2007. Her research interest is mainly on intelligent image synthesis, including image-based rendering, image navigation, pre-computed lighting, and panoramic image processing. She has published journal papers in ACM TOG, IJCV, TVCG, TMM, Patter Recognition et al, and conference papers in Siggraph Asia, CVPR, ICCV, ICME, CGI. She is IEEE member, and has served in program committee of several conferences, including Siggraph Asia Technical Sketches & Posters 2011, ChinaVis 2015/2016, CAG&CD 2017.