Similarity in Parameter-Space Exploration

The exploration of parameter spaces is most effective when we have an objective function. Even a rough notion of such a function can guide the development of effective visual representations. However, if we have no clear definition of the user’s intent, we need to obtain and refine it through interactive visualization. To simplify this process for the user, we can analyze the structure of the output space by characterizing the similarity between individual samples. This not only provides us with additional information on how to visually abstract the output space, but also has the potential of assisting us in deducing relationships. This part of the session will focus on the use of spatial similarity and its applications in biology and medicine. Furthermore, it will also examine the connections between similarity and uncertainty.

Industrial applications of uncertainty visualization

Regarding industrial applications of uncertainty visualization two areas are addressed in detail: 1) quality control by metrology using industrial 3D X-ray computed tomography and 2) engineering in the field of car engine design The uncertainty of extracted surfaces and interfaces and consequently of computed dimensional measurement features is a critical issue, which is usually neglected in 3D X-ray computed tomography of industrial components. As the extracted surfaces may only represent sharp borders, which are supposed to characterize the “real” boundary of a material of interest, the entire information on the quality of the interface and thus on the uncertainty of extracted dimensions is lost. However especially for multi-material components artefacts may influence surface extraction algorithms. Special attention in this application area will be given to the following approaches: Stability Analysis of 3DCT Scan Positions and Statistical Analysis of XCT data. In car engine design, systems projecting a continuous n-dimensional parameter space to a continuous m-dimensional target space play a highly important role. If evaluating the system is expensive, an analysis is often limited to a small number of sample points. This application area is demonstrated in an approach, which enables a continuous analysis of a sampled parameter space with respect to multiple target values to guide the user to potentially interesting parameter regions.

Algorithms, Parameters, Heuristics – Quo Vadis?

Algorithms and their parameters are closely intertwined. They together constitute a path from the problem to the solution by mapping data to images. Even if parameters are ‘just auxiliary measures’ they definitely need our help. Heuristic parameter specification is a viable approach as long as some sort of sensitivity analysis is taken care of. This sensitivity analysis should not be only done in parameter and algorithm space it should also be extended to data and image space. Furthermore the sensitivity analysis should also be applied to interaction space, as we are often confronted with interactive visualization applications. The increased complexity of data ensembles, large simulation runs and uncertainty in the data poses interesting visualization challenges. How shall we cope with the increased data and analysis complexity? Three of several possible directions include integrated views and interaction, comparative visualization and fuzzy visualization. With fuzzy visualization, techniques of information theory will play a bigger role in coping with large parameter spaces. Currently problem solving in visualization is typically algorithm-centric and thus imperative by definition. With increased data complexity it will probably become more declarative and thus more data and image centric, as domain experts have always been data-centric. A data-centric approach means that the user does not specify how data is mapped to images but defines which features of the data (s)he would like to see how in the result images. This is like specifying pre- and post-conditions but not the instructions to get from the first to the second. An optimization process should then automatically figure out which algorithms and parameter settings best fulfill the user defined declarations and constraints. Frameless rendering is about efficiently rendering animation sequences where pixels are updated on a priority basis. At no point in time all pixels of the image are up-to-date, i.e., no frame is available though the animation sequence as a whole evolves. Analogously to this concept, we foresee algorithmless visualizations in the sense that not a single algorithm is explicitly specified by the user in a given application. For different features of the data and for different parts of the image the most appropriate algorithm among a set of possible candidates might be automatically selected. Various combinations and integrations of visualization algorithms might be possible to best achieve the user goals and declarations. Each pixel or voxel might get its own algorithm on demand. Interval arithmetic has long been used to cope with uncertainties due to rounding, measurement and computation errors. Handling ensemble data in an analogous manner may lead to densely visualizing intervals or even distributions. While there are already some approaches to locally investigate visualization parameter spaces, not much has been done in terms of a global or topological analysis. For quantitative results visualization algorithms will have to provide more stability and robustness analyses in the future. With the increased data complexity (massive-multiple, heterogeneous data) heuristic approaches and parameter space analyses will become even more important. This raises the need to visualize uncertain, fuzzy, and even contradictory information. Very often heuristics are useful. But even if you do not (exactly) know what you are doing (this is what heuristics is about), you should make sure that it is safe what you are doing. Safety concerns robustness, stability, and sensitivity of an algorithm and its parameters. So heuristics are great, when handled with care.

• Similarity in Parameter-Space Exploration
• Stability Analysis of 3DCT Scan Positions
• Statistical Analysis of Industrial CT data
• Algorithms, Parameters, Heuristics – Quo Vadis?

The main contribution of Session 6 Closing Session is besides the second part of the parameter space exploration and various industrial applications of uncertainty visualization an outlook in this domain.

Similarity in Parameter Space Exploration:
• M. Haidacher, S. Bruckner, and M. E. Gröller, Volume analysis using multimodal surface similarity, IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 6, pp. 1969-1978, 2011. [Link]
• S. Bruckner and T. Möller, Isosurface similarity maps, Computer Graphics Forum, vol. 29, no. 3, pp. 773-782, 2010.
Link]
• S. Bruckner, V. Šoltészová, M. Gröller, J. Hladuvka, K. Bühler, J. Y. Yu, and B. J. Dickson, BrainGazer – Visual queries for neurobiology research, IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1497-1504, 2009. [Link]

Industrial applications of uncertainty visualization:
• Artem Amirkhanov, Christoph Heinzl, Michael Reiter, Eduard Gröller, Visual Optimality and Stability Analysis of 3DCT Scan Positions, IEEE Transactions on Visualization and Computer Graphics, 2010, 16, pages 1477-1487, October 2010. [Link]
• Berger, W, Piringer, H, Filzmoser, P, and Gröller, E (2011). Uncertainty-Aware Exploration of Continuous Parameter Spaces Using Multivariate Prediction, Published in Computer Graphics Forum, 30(3): pp. 911-920. [Link]
• Christoph Heinzl, Johann Kastner, Torsten Möller, Eduard Gröller, Statistical Analysis of Multi-Material Components using Dual Energy CT, In VMV 2008, Vision, Modeling and Visualization, pages 179-188. October 2008. [Link]

Algorithms, Parameters, Heuristics – Quo Vadis?
• Artem Amirkhanov, Stefan Bruckner, Christoph Heinzl, and M. Eduard Gröller, The Haunted Swamps of Heuristics: Uncertainty in Problem Solving (submitted as book chapter)

Vienna University of Technology

Stefan Bruckner received his master's degree in Computer Science from the Vienna University of Technology (VUT), Austria in 2004 and his Ph.D. in 2008 from the same university. He is currently an Assistant Professor at the Institute of Computer Graphics and Algorithms at VUT. In 2009/2010, he spent one year as a visiting Postdoctoral Research Fellow at Simon Fraser University, Canada. His research interests include biomedical and illustrative visualization, volume rendering, and visual data exploration. In 2011, he was awarded the Eurographics Young Researcher Award. He is a member of the IEEE Computer Society, ACM SIGGRAPH, and Eurographics.

University of Applied Sciences – Upper Austria

Christoph Heinzl received his PhD degree in Computer Science from the Vienna University of Technology (VUT) in 2009 in the field of visualization and analysis of industrial computed tomography data. He is now senior researcher at the University of Applied Sciences – Upper Austria. His research interests include uncertainty visualization, visualization of industrial computed tomography data, image processing, dimensional measurement, simulation, 3D reconstruction. Christoph Heinzl is IEEE and Eurographics member.

Vienna University of Technology

Eduard Gröller is professor at the Vienna University of Technology, Austria, and adjunct professor of computer science at the University of Bergen, Norway. His research interests include computer graphics, scientific visualization, medical visualization, information visualization and visual analytics. He co-authored more than 200 scientific publications and acted as a co-chair, IPC member, and reviewer for numerous conferences and journals in the field. Dr. Gröller has been Co-Chief Editor of the Computer Graphics Forum journal (2008-2011) and chair of the EuroVis 2012 conference.