CGIV.jpg

CGiV2012  -  9th International Conference
Computer Graphics, Imaging and Visualization

www.graphicslink.co.uk/cgiv2012/
24, 25 - 27 July 2012

● National Chiao Tung University ● Hsinchu ● Taiwan 

www.graphicslink.co.uk/cgiv2012/VENUE.htm

 

Keynotes Lectures:

 

1.   Window to the soul: tracking eyes to inform the design of visualizations

Tony Huang, CSIRO ICT Centre, Australia

 

2.   Visualization of Hierarchical Information on Mobile Screens

Kang Zhang, Professor and Director of Visual Computing Lab, University of Texas at Dallas , USA

 

3.   The Anatomy of a Graph Visualization System

Giuseppe Liotta, Professor of computer Science at the Faculty of Engineering of the University of Perugia

 

4.    Remote Sensing and Image Processing Techniques

Wong Chow Jeng , Universiti Sains Malaysia, Malaysia

 

5.    Deriving Structure of Images via Dictionary and Bayesian Network learning

Wen-Liang Hwang, Institute of Information Science, Academia Sinica, Taiwan

 

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Window to the soul: tracking eyes to inform the design of visualizations

Tony Huang

 CSIRO ICT Centre, Australia

 

Abstract:

A picture is worth a thousand words. To take advantage of powerful human vision, we generate visualizations for people to view and to understand the underlying data. However, these a thousand words do not necessarily tell the truth about the data. A good visualization can make the data understanding process effective, while a bad visualization may hinder the process, even convey misleading information.   To produce effective visualizations, it is important to have a good understanding of how people actually perceive and process the visual information. People view visualizations using their eyes, and tracking their eye movements can be a useful method for this purpose. In this talk, I will present a series eye tracking studies on how people read graphs. These studies demonstrate that:  1) eye tracking is an effective method for gaining insights into how people read graphs,  and 2) how obtained insights from eye tracking can be used to inform the design of visualizations.

 

Text Box:  Short Biography:

Dr. Tony Huang is a research scientist at CSIRO ICT Centre, Australia. He is one of the leading researchers in the User Interaction and Collaboration group. His research interests lie in human-cantered computing in general, and HCI, human factors and visual perception and cognition in particular. He has published a number of quality papers in these areas. Currently, he is editing/authoring four scholarly books, chairing a special session on ubiquitous and collaborative computing at IEEE SMC'12, and chairing technical programs for CGIV'12 and OzCHI'12.

 

 

 

 

 

 

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Visualization of Hierarchical Information on Mobile Screens

Kang Zhang

Professor and Director of Visual Computing Lab

The University of Texas at Dallas, USA

Abstract:

Visualizing and exploring a hierarchical structure on small screen devices, such as mobile phones, is a challenge. On the screens of desktop PCs and laptops, such hierarchical structures are often shown in a tabular view. Due to the size particularly the width restriction, a tabular view is not suited for mobile screens. This talk discusses a visualization technique that displays multiple levels of a hierarchy on a single view and allows users to explore the hierarchical structure rapidly through touch input. The visualization technique makes full use of the available space and flexibly allocates the space for individual nodes according to the application criteria. The approach adapts the selection and display of relevant information based on the user’s query habit, by hiding less important information to maximize the utilization of the space.

 

KZ09smallShort Biography:

Kang Zhang is Professor and Director of Visual Computing Lab, Department of Computer Science at the University of Texas at Dallas. He is also a Board Director of Vital Art and Science Inc., USA. He holds a B.Eng. degree in Computer Engineering from University of Electronic Science and Technology of China, a Ph.D. degree from University of Brighton, UK, and an Executive MBA degree from the University of Texas at Dallas. Prior to joining UT-Dallas, he held various academic positions in the UK, Australia, and China. Dr. Zhang's current research interests include information visualization, visual languages, aesthetic computing, and managerial aesthetics; and has published over 180 papers and 5 books in these areas. He is also accomplished artist, having won various awards. Dr Zhang is on the Editorial Boards of Journal of Visual Languages and Computing, International Journal of Software Engineering and Knowledge Engineering, and International Journal of Advanced Intelligence. His home page is at www.utdallas.edu/~kzhang.

 

 

 

 

 

 

 

 

 

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The Anatomy of a Graph Visualization System

Giuseppe Liotta

Professor of computer Science at the Faculty of Engineering of the University of Perugia

 

Abstract:

Graph visualization addresses the problem of efficiently conveying the structure  of relational information, which is typically modeled by networks. Therefore,  graph visualization systems are largely used for information exploration and  knowledge discovery, particularly in those applications that need to manage,  process and analyze large sets of data. The design of a graph visualization  systems typically addresses questions that belong to the intersection of different  disciplines, such as graph algorithms, data mining, software engineering,  algorithm engineering, and visual analytics. In this talk I will shortly review

some common and emerging graph visualization paradigms, discuss general  design principles  present application examples, and compare  different models  for the realization of effective graph visualization systems.

 

Text Box:  Short Biography:

Giuseppe Liotta received a Ph.D. degree in computer engineering from the University of Rome ”La Sapienza” in 1995. After two years of post-doc at Brown University, he first joined the University of Rome ”La Sapienza” as assistant professor (1996-1998) and then the University of Perugia as associate professor (1998-2002). Since 2002 he is a professor of computer engineering at the Faculty of Engineering of the University of Perugia.

His research interests are mainly directed at the analysis and design of algorithms and systems that have applications in the fields of graph theory and graph drawing, information visualization and visual analytics, and computational geometry. On these topics he edited special issues, wrote book chapters and surveys, published more than 150 research papers, and gave lectures worldwide. He regularly serves as PC member or as general chair of international conferences, is steering committee member of the International Symposium of Graph Drawing, and managing editor of the Journal of Graph Algorithms and Applications. During the years, his research has been funded by several public and private sponsors.

 

 

 

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Remote Sensing and Image Processing Techniques

CHOW JENG, WONG, Universiti Sains Malaysia, Malaysia

Abstract:

Migratory raptor counting has been widely conducted in many countries for years. The surveys are conducted yearly during migratory season for the purpose of conservation and bird flu prevention. However, the accuracy of the ground count is affected by many parameters, including the distance of the flock from the observer, the size of the species, the light intensity, the interval distance between members of the flock, the migratory birds’ speed, and the speed of wandering of individuals. This method also strongly depends on the individual experience and skills of the observer. Therefore, it is important to have an effective method to solve these problems. In this study, we developed a migratory raptor counting system by using Digital Single Lens Reflex (DSLR) camera and image processing techniques. DSLR camera was used as a remote sensor to capture the migratory raptors images. The developed software system will analyse each raptor image and count the migratory raptors in the image. This counting system has been validated with the manual count method for the accuracy. The results show that the Pearson correlation coefficient of the system is 97.3%. The results show that this monitoring system can provide an alternative way for counting migratory raptors.

Text Box:

Short Biography:

WONG CHOW JENG ( 黄召仁 ) has studied  Physics in Universiti Malaya, Kuala Lumpur, Malaysia.

 Over 15 years of working experience in multinational manufacturing companies for semiconductors and electronics assembly. He worked as a Factory Manager in Varitronix EC,  Penang (Subsidiary of Magna Donnelly- USA), as an Industrial Engineering Department Manager in SONY Electronics, Penang, as a Quality Assurance Senior Engineer in HITACHI Semiconductor, Penang, and as a Research & Development Engineer in SIEMENS Semiconductor, Penang.

In 1990, Wong successfully redesigned, built and tested a plasma focus fusion device for ICTP (International Centre of Theoretical Physics) in Italy. He also successfully carried out some researches in ICTP. http://www2.ictp.it/NewSIS/personnel_details.mhtml?pk=WONGGGCHOW001m

 Currently, his research interests include remote sensing application for air pollution monitoring via networking. In year 2006, this project was chosen to participate in the competition of 17th International Invention, Industrial Design & technology Exhibition 2006 at KLCC, Kuala Lumpur. This project won three awards in this competition. 

He has published more than Sixty articles on these areas at national and international proceedings. Eight of his publication was selected to published in the Smithsonian/NASA Astrophysics Data System hosted by the Harvard-Smithsonian Center.

 

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Deriving Structure of Images via Dictionary and Bayesian Network Learning

Wen-Liang Hwang, Institute of Information Science, Academia Sinica, Taiwan

 

The most important descriptor of an image is its structure. Image processing researchers have developed several methods to derive the low-dimensional structure of images; examples include using the Fourier transform to represent oscillatory components in images, a wavelet transform to represent piecewise smooth images, and a pre-defined dictionary for sparse representation. Such approaches have achieved a certain degree of success in deriving image structures and solving low-level problems, such as compression, and restoration; however, there is now a growing trend towards data-driven approaches that exploit data-adaptive algorithms to retrieve image structures.

 

In this talk, I will present two data-adaptive methods: one learns an image’s structure via dictionary adaptation, and the other learns a Bayesian network from transform coefficients. I will show that the state-of-the-art K-SVD dictionary learning algorithm can be improved by using the proximal-point method; and by exploring the structures of wavelet coefficients, I will show that the Bayesian network approach outperforms the state-of-the-art BM3D denoising algorithm, particularly on texture images.

 

Dr. Wen-Liang Hwang was awarded a B.S. degree in Nuclear Engineering by National Tsing Hua University, Hsinchu, Taiwan in 1981, an M.S. degree in Electrical Engineering by Polytechnic University, Brooklyn, New York in 1988, and Ph.D. degree in computer science from New York University, New York in 1993. In 1995, he joined the Institute of Information Science, Academia Sinica, Taiwan, where he is now a research fellow.

 

Dr. Hwang’s research area include wavelet analysis, and signal and image processing. As well as co-authoring one book and contributing a chapter to another book, he has published several technical papers in the leading journals and conferences of the IEEE Society. In 2001, he received a national award for distinguished junior researchers in Taiwan.

 

 

 

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