Kolloquiumszyklus - previous dates

We introduce the design and implementation of Proscene-3, a highly customizable open source framework for interactive environments comprising three layers: a low-level component providing a set of virtual events which represent all sorts of input sources and the means to bind user-defined actions to them; a mid-level component, implementing a feature-rich set of widely-used motion actions allowing picking & manipulation of objects, including the scene viewpoint; and, a high-level library, exposing those features to the Processing language.

In this talk, I give an overview of perceptually motivated techniques for the visualization of medical image data. These techniques include physics-based lighting and illustrative rendering that incorporate spatial depth and shape cues. In addition, I discuss evaluations that were conducted in order to study the perceptual effects of these visualization techniques as compared to conventional techniques. These evaluations assessed depth and shape perception with depth judgment, orientation matching, and related tasks. This overview of existing techniques and their evaluation serves as a basis for defining the evaluation process of medical visualizations.

Molecular dynamics simulations allow scientists to run virtual experiments that can even reproduce the interactions in molecular systems with previously unknown behavior. That is, these simulations provide us with a “Computational Microscope” that enables studying the dynamic behavior of proteins and other biomolecules down to individual atoms. Interactive visualization is an essential part of this Computational Microscope, since it allows domain experts to explore the results of their simulations. Direct visualizations of the data using established molecular models show the dynamics of the simulated molecules. While such direct visualizations can already reveal many interesting processes, interactive analysis can further enhance the exploratory data analysis with the results of feature extractions. In my talk, I will discuss algorithms for direct molecular visualization as well as several examples of interactive visual analysis methods for biomolecular simulation data, including real-time cavity detection algorithms and comparative visualizations. I will also present actual use cases where interactive visual analysis led to the discovery of unexpected phenomena in the simulated molecular systems.

Mapping from data tables to visual abstraction is a crucial step in the visualization pipeline. It often determines if a visualization will be successful or not.

Basic visual structures: points, lines, shapes, and color are combined into more complex visual representations. We explain standard representations for 1D, 2D, 3d, and nD data, such as, histogram, box-plot, pie-chart, scatter-plot, or parallel coordinates, for example.
Further, we provide guidelines how to use them and explain how not use certain visual structures.

Problems of using 2D or 3D structures to depict 1D data, or the problem of line-width illusion are also explained. The students should gain basic understanding of the importance of the visual-mapping step in the visualization pipeline, and they should be able to choose right visualization, and to recognize misleading visualization.

Most of the cellular processes are driven by protein activities. For example, chemical energy is generated by ATP synthases, replication is driven by PCNA clamp, transcription is regulated by transcription factors, DNA is structured by histones and SMC proteins etc. Most of the cellular proteins, however, exist and function as multi-subunit complexes (such as ATP pump, replisome, enhanceosome, nucleosome etc.). Such complexes are assembled thru multiple protein interactions, which determine their architecture, function and dynamics.

In this lecture, several protein complexes will be shown in a bottom up way i.e. starting from single protein/subunit interactions to complexes and further to large molecular assemblies. I will review state-of-the-art experimental methods for protein/complex analysis and their output formats, including animations of molecular machines. Use of visualization techniques for protein complex animations and their potential for description of dynamic cellular processes will be discussed.

Within this talk I will cover our recent work in the area of molecular visualization. Two techniques will be presented, which have been developed with the goal to improve the spatial comprehension of complex molecular structures. First, coverage-based opacity estimation is discussed as a technique to achieve Depth of Field (DoF) effects when visualizing molecular structures. The proposed algorithm is an object-based approach which eliminates many of the shortcomings of state-of-the-art image-based DoF algorithms. Based on observations derived from a physically-correct reference renderer, coverage-based opacity estimation exploits semi-transparency to simulate the blur inherent to DoF effects. It achieves high quality DoF effects, by augmenting each atom with a semi-transparent shell, which has a radius proportional to the distance from the focal plane of the camera. Thus, each shell represents an additional coverage area whose opacity varies radially, based on our observations derived from the results of multi-sampling DoF algorithms. Second, I will discuss the integration of diffuse illumination effects into molecular visualization. While current molecular visualization techniques utilize ambient occlusion as a global illumination approximation in order to improve spatial comprehension, interreflections are also known to improve the spatial comprehension of complex geometric structures. To realize these interreflections in real-time, an analytic approach is exploited for capturing interreflections of molecular structures. By exploiting the knowledge of the underlying space filling representations, the required parameters can be reduced and symbolic regression can be applied to obtain an analytical expression for interreflections. I will discuss how to obtain the data required for the symbolic regression analysis, and how to exploit the analytic solution to enhance interactive molecular visualizations. For both presented techniques, high quality results will be shown, which a re visually comparable to those of state-of-the-art offline renderers.

3D printing is considered a disruptive technology with potentially tremendous socioeconomic impact. In recent years, additive manufacturing technologies have made significant progress in terms of both sophistication and price; they have advanced to a point where devices now feature high-resolution, full-color, and multi-material printing. Nonetheless, they remain of limited use, given the lack of efficient algorithms and intuitive tools that can be used to design and model 3D-printable content.

My vision is to unleash the full potential of 3D printing technology with the help of computational methods. In our research, we are working to invent and develop new computational techniques for intuitively designing virtual 3D models and bringing them to the real world. Given the digital nature of the process, three factors play a central role: computational models and efficient representations that facilitate intuitive design, accurate and fast simulation techniques, and intuitive authoring tools for physically realizable objects and materials.

In this talk, I will present several projects that demonstrate our recent efforts in working toward this goal, structured according to basic object properties, and the lessons learned from working over several years with various 3D printers.

This talk presents several optimization approaches to customizing and designing the traveling guide maps. The idea behind our approach is to formulate design criteria commonly employed by illustrators as mathematical constraints first and then optimizing the cost function in order to fully enhance the readability of the map layout. We consider two design strategies for this purpose. The first one is for route handling, and the second one is for label placement. To visually guide users’ attention, we try to emphasize the user specified route in the first design, which is accomplished by introducing linear programming(LP) optimization and mixed-integer programming(MIP) technique. As for the second design, we employ genetic algorithm(GA) and again MIP in order to maximally placing thumbnail photographs close to their corresponding stations on the metro maps. Several design examples are also presented to demonstrate the feasibility of our prototype system together with user studies on how users are satisfied with the formulated design criteria.

Recent advances in scanning, modeling, rendering, and hardware make it possible to generate nearphotorealistic images of moderately complex scenes at interactive rates. One of the next grand challenges in computer graphics and visualization is to model vibrant, dynamic scenes of realworld complexity, such as urban spaces. The problem of modeling virtual cityscapes offers a diverse set of opportunities for innovations and provides enabling technologies of societal interests, including energy use, transportation mechanisms, economic sustainability, education and entertainment. Some of the key research issues include interactive simulation of large-scale crowds, realistic modeling of complex traffic flows, efficient motion synthesis of plausible pedestrian behaviors and natural phenomena. At least one to two orders of magnitude performance improvement in hardware will be needed. New algorithms and software systems that can exploit such computing power must be developed.

In this talk, I will survey some of recent efforts on addressing the problem of modeling, simulating, and directing virtual agents in complex dynamic environments. In particular, I will present several complementary approaches for representing movement of multiple virtual entities, including both crowds and traffic, in urban scenes and city highways. I will further highlight the design of scalable algorithms for these problems by taking advantages of parallelism available on emerging commodity hardware, such as GPUs and many-core processors. These methods can be applied to interactive crowd simulation, motion synthesis, and coordination of multiple autonomous agents in computer games and virtual environment systems. I will conclude by discussing our experiences and some future research directions on incorporating sound effects and natural phenomena.

Magnetic Resonance Imaging (MRI) is a primary tool for clinical investigation of the brain and fetal organs. High resolution imaging with volumetric coverage using stacks of slices or true three dimensional (3D) methods is widely available and provides rich data for image analysis. However such detailed volumetric data generally takes several minutes to acquire and requires the subject to remain still or move only small distances during acquisition. Fetal organ imaging introduces a number of additional challenges. Maternal breathing may move the fetus and the fetus itself can and does spontaneously move during imaging. These movements are unpredictable and may be large, particularly involving substantial head and body rotations. Motion correction methods have revolutionized MRI of the fetus  by reconstructing a high-resolution 3D volume of  fetal organs from such motion corrupted stacks of 2D slices. Such reconstructions are valuable for both clinical and research applications. However, reconstruction is computationally expensive and can only be performed off line. Information about the accuracy of the scan and potential uncertainties is unknown or not considered in the clinical practice.

In this talk I will discuss the fundamentals of fetal MRI reconstruction and it's parallelization and hardware acceleration for a future on-line application during the scan. Furthermore, I am looking forward to a discussion about potential application of novel visualization techniques to communicate varying uncertainties of the reconstruction to examining radiologists and scientists.

The human visual system (HVS) has its own limitations (e.g., the quality of eye optics, the luminance range that can be simultaneously perceived, and so on), which to certain extent reduce the requirements imposed on display devices. Still a significant deficit of reproducible contrast, brightness, spatial pixel resolution, and depth ranges can be observed, which fall short with respect to the HVS capabilities.  Moreover, unfortunate interactions between technological and biological aspects create new problems, which are unknown for real-world observation conditions.

In this talk, we are aiming at the exploitation of perceptual effects to enhance apparent image qualities.  At first, we show how the perceived image contrast and brightness  can be improved by exploiting the Cornsweet and glare illusions. Then, we present techniques for hold-type blur reduction, which is inherent for LCD displays.  Also, we investigate apparent resolution enhancements, which enable showing image details beyond the physical pixel resolution of the display device.  Finally, we discuss the problem of perceived depth enhancement in stereovision, as well as comfortable handling of specular effects, film grain, and video cuts.
 

We introduce a complete morphological analysis framework for 3D point clouds. Starting from an unorganized point set sampling a surface, we propose morphological operators in the form of projections, allowing to sample erosions, dilations, closings and openings of an object without any explicit mesh structure. Our framework supports structuring elements with arbitrary shape, accounts robustly for geometric and morphological sharp features, remains efficient at large scales and comes together with a specific adaptive sampler. Based on this meshless framework, we propose applications which benefit from the non-linear nature of morphological analysis and can be expressed as simple sequences of our operators, including medial axis sampling, hysteresis shape filtering and geometry-preserving topological simplification.

Computational Science and Visualization have been the focus of my studies and research for the last several years. The aim of Computational Science is to gain insight into complex systems and natural phenomena by capturing them in computational models and execute these models on the computer.  With Visualization I mean computer visualization in the broadest sense, including branches like Computer Graphics and Image Processing. During my talk I visit several of my projects, ranging from cloth simulation and fluid simulation to Deferred Shading and (non-photorealistic) rendering. One of the themes of my talk is how the programmable graphics processing unit narrowed the gap between realistic physically-based simulations and real-time computer graphics.

Streamlines and Streamsurfaces are standard tools for the visual analysis of flow data. Nevertheless, their applications still poses challenges concerning their extraction, integration, and visualization.

In the talk, we tackle three problems:
- the selection of suitable stream lines,
- a stable integration of stream surfaces,
- the selection of suitable stream surfaces.

We show that these problems can and should be formulated as global optimization problems. We present the respective error functionals to be minimized and show solutions for several test cases.
 

The vision of integrating the best of automated computation and capabilities of the human has been a highly praised goal in visualization research and parallels the emergence of visual analytics as a field on its own. In visual analytics, the integration of automated and interactive methods is considered to be one of the main mechanisms to facilitate the construction of knowledge in data analysis. This integration can be done at different levels -- from static visualizations of computation results to giving user the interactive control on the inner workings of an algorithm.

One form of such integrations involve the "seamless" use of automated computational tools within interactive visual data analysis. These methods employ "task-oriented" computations whose results become natural elements of the interactive process. Such approaches are important to build more reliable and insightful visual-data-analysis routines and to foster the use of visual analytics by a wider audience. In this talk, I will provide a quick overview of the different types of integration in visual analysis and focus on the examples of using computational tools seamlessly. The talk will then move on to discuss opportunities and open issues with such approaches.

The multidisciplinary BLUR project at UC Berkeley combines computer graphics with optics, optometry, and photography.  This research investigates mathematical models to describe the shape of the cornea and algorithms for cornea measurement, scientific and medical visualization for the display of cornea shape, mathematics and algorithms for the design and fabrication of contact lenses, simulation of vision using actual patient data measured by wavefront aberrometry, photo-realistic rendering algorithms for generating imagery with optically-correct depth of field, view camera simulation.  This talk will present an overview of rendering algorithms for simulating depth of field found in photographs and of vision-realistic rendering algorithms for simulating a subject's vision.   Recent work on correcting visual aberrations with computational light field displays will also be briefly introduced.

There are two parts to this talk. The first gives an overview of my Master’s thesis, which is about visualizing stream surfaces. The second is about modifications and additions to Paraview that I did as part of my internship at the Swiss National Supercomputing Center.

Stream surfaces are used to visualize snapshots in time of fluid flow. However, as useful as they are, they also have significant problems. In particular, humans have a difficult time understanding these surfaces and often make errors.

The thesis investigated the hypothesis that artificial coloring can help viewers perceive stream surfaces.  I present three algorithms for deciding on and placing colors based on a surface’s curvature or the use of simulated viewpoints. I will also discuss a user study that I designed and carried out that tested how users perceive images of surfaces that were created with these algorithms.

My work with Paraview involved improving the control of opacity in volume rendering. When using transfer functions to determine opacity in volume rendering, it can be difficult to get a good visualization with just one transfer function. Therefore, it can be useful to use two different transfer functions based on different aspects of the data, such as scalar and gradient values. I will describe and demonstrate a modification to Paraview’s GUI interface that allows users to use two different transfer functions and a new feature in Paraview that allows users to specify a two dimensional transfer function.

In the field of volume visualization the data is usually given as discrete samples. During rendering a continuous reconstruction is necessary. It is usually achieved by convolving the discrete data with a continuous filter. The assumption is that the discrete data is derived from a continuous signal sampled on a lattice, where the sinal is spherically bandlimited. In volume visualization Cubic Lattice (CC), the body-centered lattice (BCC) and the face-centered lattice (FCC) are used. For comparing the various filters on the lattices a benchmark signal, the famous ML-signal is commonly used. The authors arrived in the conclusion that the pre-aliasing effect is minimal on FCC lattice. However their assumption was based on the spherically band-limitedness of underlying signal. In our work we showed that the ML-signal is not spherically band limited, and it gives an unfair advantage to the FCC lattice during comparisons. On the other hand, we proposed that rotating the ML signal can lead to a fair comparison in terms of aliasing.

Interactive visualization and analysis of large and complex volume data is an ongoing challenge. Data acquisition tools produce hundreds of Gigabytes of data and are one step ahead of visualization and analysis tools. Therefore, the amount of data to be rendered is typically beyond the limits of current computer and graphics hardware performance. We tackle this challenge in the context of state-of-the-art out-of-core multiresolution direct volume rendering by using a common mathematical framework (a) to extract relevant features from these large datasets, (b) to reduce and compress the actual amount of data, and (c) to directly render the data from the framework coefficients. As a common framework, we introduced the higher-order extension of the matrix singular value decomposition - tensor approximation (TA) - as a compact volume data representation. In particular, the bases of tensor approximation were exploited to model state-of-the-art multiresolution volume visualization and multiscale feature extraction with one set of global bases. Based on this contribution, a feature scale metric was developed to automatically select a feature scale and a resolution for the final reconstruction. Thanks to the compact data representation by TA, a significant data compression and GPU-based real-time visualization was achieved. The new algorithms were tested on volume datasets from micro-computed tomography and phase-contrast synchrotron tomography that range up to 32 Gigabytes.