Peter Mindek

Visibility equalizer cutaway visualization of mesoscopic biological models

In scientific illustrations and visualization, cutaway views are often employed as an effective technique for occlusion management in densely packed scenes. We propose a novel method for authoring cutaway illustrations of mesoscopic biological models. In contrast to the existing cutaway algorithms, we take advantage of the specific nature of the biological models. These models consist of thousands of instances with a comparably smaller number of different types. Our method constitutes a two stage process. In the first step, clipping objects are placed in the scene, creating a cutaway visualization of the model. During this process, a hierarchical list of stacked bars inform the user about the instance visibility distribution of each individual molecular type in the scene. In the second step, the visibility of each molecular type is fine‐tuned through these bars, which at this point act as interactive visibility equalizers. An evaluation of our technique with domain experts confirmed that our equalizer‐based approach for visibility specification is valuable and effective for both, scientific and educational purposes.

Contextual Snapshots: Enriched Visualization with Interactive Spatial Annotations

Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analyzed and compared in different views. However, the semantics of such selections are often dependent on other parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means for managing spatial selections in visualized data. The selections are automatically associated with the context in which they have been created. Contextual snapshots can be also used as the basis for interactive integrated and linked views, which enable in-place investigation and comparison of multiple visual representations of data. Our approach is implemented as a flexible toolkit with well-defined interfaces for integration into existing systems. We demonstrate the power and generality of our techniques by applying them to several distinct scenarios such as the visualization of simulation data and the analysis of historical documents.

Automatized summarization of multiplayer games

We present a novel method for creating automatized gameplay dramatization of multiplayer video games. The dramatization serves as a visual form of guidance through dynamic 3D scenes with multiple foci, typical for such games. Our goal is to convey interesting aspects of the gameplay by animated sequences creating a summary of events which occurred during the game. Our technique is based on processing many cameras, which we refer to as a flock of cameras, and events captured during the gameplay, which we organize into a so-called event graph. Each camera has a lifespan with a certain time interval and its parameters such as position or look-up vector are changing over time. Additionally, during its lifespan each camera is assigned an importance function, which is dependent on the significance of the structures that are being captured by the camera. The images captured by the cameras are composed into a single continuous video using a set of operators based on cinematographic effects. The sequence of operators is selected by traversing the event graph and looking for specific patterns corresponding to the respective operators. In this way, a large number of cameras can be processed to generate an informative visual story presenting the gameplay. Our compositing approach supports insets of camera views to account for several important cameras simultaneously. Additionally, we create seamless transitions between individual selected camera views in order to preserve temporal continuity, which helps the user to follow the virtual story of the gameplay.

ViviSection: skeleton-based volume editing

Volume segmentation is important in many applications, particularly in the medical domain. Most segmentation techniques, however, work fully automatically only in very restricted scenarios and cumbersome manual editing of the results is a common task. In this paper, we introduce a novel approach for the editing of segmentation results. Our method exploits structural features of the segmented object to enable intuitive and robust correction and verification. We demonstrate that our new approach can significantly increase the segmentation quality even in difficult cases such as in the presence of severe pathologies.

Managing Spatial Selections With Contextual Snapshots

Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analysed and compared in different views. However, the semantics of such selections often depend on specific parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means for managing spatial selections in visualized data. The selections are automatically associated with the context in which they have been created. Contextual snapshots can also be used as the basis for interactive integrated and linked views, which enable in‐place investigation and comparison of multiple visual representations of data. Our approach is implemented as a flexible toolkit with well‐defined interfaces for integration into existing systems. We demonstrate the power and generality of our techniques by applying them to several distinct scenarios such as the visualization of simulation data, the analysis of historical documents and the display of anatomical data.

Data-sensitive visual navigation

A model for navigation sensitive to the underlying data is proposed.A user-interface element, TreeSlider, for traversing tree structures is proposed.The data-sensitive navigation model is applied to various user-interface elements. Display Omitted In visualization systems it is often the case that the changes of the input parameters are not proportional to the visual change of the generated output. In this paper, we propose a model for enabling data-sensitive navigation for user-interface elements. This model is applied to normalize the user input according to the visual change, and also to visually communicate this normalization. In this way, the exploration of heterogeneous data using common interaction elements can be performed in an efficient way. We apply our model to the field of medical visualization and present guided navigation tools for traversing vascular structures and for camera rotation around 3D volumes. The presented examples demonstrate that the model scales to user-interface elements where multiple parameters are set simultaneously.

Illustrative transitions in molecular visualization via forward and inverse abstraction transform

A challenging problem in biology is the incompleteness of acquired information when visualizing biological phenomena. Structural biology generates detailed models of viruses or bacteria at different development stages, while the processes that relate one stage to another are often not clear. Similarly, the entire life cycle of a biological entity might be available as a quantitative model, while only one structural model is available. If the relation between two models is specified at a lower level of detail than the actual models themselves, the two models cannot be interpolated correctly. We propose a method that deals with the visualization of incomplete data information in the developmental or evolutionary states of biological mesoscale models, such as viruses or microorganisms. The central tool in our approach is visual abstraction. Instead of directly interpolating between two models that show different states of an organism, we gradually forward transform the models into a level of visual abstraction that matches the level of detail of the modeled relation between them. At this level, the models can be interpolated without conveying false information. After the interpolation to the new state, we apply the inverse transformation to the models original level of abstraction. To show the flexibility of our approach, we demonstrate our method on the basis of molecular data, in particular data of the HIV virion and the mycoplasma bacterium.

Multiscale Molecular Visualization

We provide a high-level survey of multiscale molecular visualization techniques, with a focus on application-domain questions, challenges, and tasks. We provide a general introduction to molecular visualization basics and describe a number of domain-specific tasks that drive this work. These tasks, in turn, serve as the general structure of the following survey. First, we discuss methods that support the visual analysis of molecular dynamics simulations. We discuss, in particular, visual abstraction and temporal aggregation. In the second part, we survey multiscale approaches that support the design, analysis, and manipulation of DNA nanostructures and related concepts for abstraction, scale transition, scale-dependent modeling, and navigation of the resulting abstraction spaces. In the third part of the survey, we showcase approaches that support interactive exploration within large structural biology assemblies up to the size of bacterial cells. We describe fundamental rendering techniques as well as approaches for element instantiation, visibility management, visual guidance, camera control, and support of depth perception. We close the survey with a brief listing of important tools that implement many of the discussed approaches and a conclusion that provides some research challenges in the field.

Metamorphers: storytelling templates for illustrative animated transitions in molecular visualization

In molecular biology, illustrative animations are used to convey complex biological phenomena to broad audiences. However, such animations have to be manually authored in 3D modeling software, a time consuming task that has to be repeated from scratch for every new data set, and requires a high level of expertise in illustration, animation, and biology. We therefore propose metamorphers: a set of operations for defining animation states as well as the transitions to them in the form of re-usable storytelling templates. The re-usability is two-fold. Firstly, due to their modular nature, metamorphers can be re-used in different combinations to create a wide range of animations. Secondly, due to their abstract nature, metamorphers can be re-used to re-create an intended animation for a wide range of compatible data sets. Metamorphers thereby mask the low-level complexity of explicit animation specifications by exploiting the inherent properties of the molecular data, such as the position, size, and hierarchy level of a semantic data subset. We demonstrate the re-usability of our technique based on the authoring and application of two animation use-cases to three molecular data sets.

Graph visualization using the metaphor of biological neural nets

Graph visualization is a large and evolving research area. Many graph visualization approaches have been developed. Most graph visualization methods use simple geometrical shapes to draw graphs. However, the potential of metaphorical visualization is often not utilized in graph visualizations. In this paper we propose a metaphorical visualization of graph structures inspired by biological neural networks. The proposed graph visualization aims at presenting graphs in a more attractive and probably also more comprehensible way.