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Fibrous structures are ubiquitous in cell biology and play essential structural and functional roles in the life cycle of a cell. They are long polymers, such as DNA carrying genetic information, or filaments forming the cytoskeleton, crucial for cell division and maintaining the cell shape. In order to disseminate new findings of such structures to peers or a general audience, animated 3D models of these structures have to be created, as they are too small to be imaged with microscopes. However, this is a tedious task carried out by scientific animators, who manually create expressive visual representations of biological phenomena. In this work, we present a novel concept which simplifies the process of animating multi-scale procedural models of biological fibrous structures. In contrast with existing work in the domain of molecular visualization, our approach can also capture dynamics, which are important to show when communicating biological processes., EuroVis 2019 - Posters, Posters, 73, 75, Tobias Klein, Ivan Viola, and Peter Mindek, CCS Concepts: Computing methodologies --> Procedural animation

Visual clutter is a major problem in large biological data visualization. It is often addressed through the means of level of detail schemes coupled with an appropriate coloring of the visualized structures. Ambient occlusion and shadows are often used to improve the depth perception. However, when used excessively, these techniques are sources of visual clutter themselves. In this paper we present a new approach to screen-space illumination algorithms suitable for use in illustrative visualization. The illumination effect can be controlled so that desired levels of semantic scene organization cast shadows while other remain flat. This way the illumination design can be parameterized to keep visual clutter, originating from illumination, to a minimum, while also guiding the user in a multiscale model exploration. We achieve this by selectively applying occlusion shading based on the inherent semantics of the visualized hierarchically-organized data. The technique is in principle generally applicable to any hierarchically organized 3D scene and has been demonstrated on an exemplary scene from integrative structural biology., Eurographics Workshop on Visual Computing for Biology and Medicine, Biology, 197, 201, Thomas Bernhard Koch, David Kouril, Tobias Klein, Peter Mindek, and Ivan Viola, CCS Concepts: Human-centered computing --> Scientific visualization

Visualization algorithms are parameterized to offer universality in terms of handling various data types, showing different aspects of the visualized data, or producing results useful for domain experts from different fields. Hence, input parameters are an important aspect of the visualization process. Their exploration and management are tasks which enable the visualization reusability, portability, and interdisciplinary communication. With increasing availability of visualization systems, which are suitable for a great variety of tasks, their complexity increases as well. This usually involves many input parameters necessary for the meaningful visualization of data. Multiple input parameters form parameter spaces which are too large to be explored by brute-force. Knowing the properties of a parameter space is often beneficial for improving data visualization. Therefore, it is important for domain experts utilizing data visualization to have tools for automatic parameter specification and for aiding the manual parameter setting. In this thesis, we review existing approaches for parameter-space visualization, exploration, and management. These approaches are used with a great variety of underlying algorithms. We focus on their applicability to visualization algorithms. We propose three methods solving specific problems arising from the fact that the output of a visualization algorithm is an image, which is challenging to process automatically and often needs to be analyzed by a human. First, we propose a method for the exploration of parameter-spaces of visualization algorithms. The method is used to understand effects of combinations of parameters and parts of the internal structure of the visualization algorithms on the final image result. The exploration is carried out by specifying semantics for localized parts of the visualization images in the form of positive and negative examples influenced by a set of input parameters or parts of the visualization algorithm itself. After specifying the localized semantics, global effects of the specified components of the visualization algorithm can be observed. The method itself is independent from the underlying algorithm. Subsequently, we present a method for managing image-space selections in visualizations and automatically link them with the context in which they were created. The context is described by the values of the visualization parameters influencing the output image. The method contains a mechanism for linking additional views to the selections, allowing the user an effective management of the visualization parameters whose effects are localized to certain areas of the visualizations. We present various applications for the method, as well as an implementation in the form of a library, which is ready to be used in existing visualization systems. Our third method is designed to integrate dynamic parameters stored during a multiplayer video game session by the individual participating players. For each player, the changing paiii rameter values of the game describe their view of the gameplay. Integrating these multiple views into a single continuous visual narrative provides means for effective summarization of gameplays, useful for entertainment, or even gameplay analysis purposes by semi-professional or professional players. We demonstrate the utility of our approach on an existing video game by producing a gameplay summary of a multiplayer game session. The proposed method opens possibilities for further research in the areas of storytelling, or at a more abstract level, parameter integration for visual computing algorithms.

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. publishedVersion