Finding Nano-\"Otzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography

Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning where we combine the advantages of two segmentation algorithms. A first weak segmentation algorithm provides good results for propagating sparse user provided labels to other voxels in the same volume. This weak segmentation algorithm is used to generate dense pseudo labels. A second powerful deep-learning based segmentation algorithm can learn from these pseudo labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses the deep-learning based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through histogram analysis. Finally, our visualization uses gradient-free ambient occlusion shading to further suppress visual presence of noise, and to give structural detail desired prominence. The cryo-ET data studied throughout our technical experiments is based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques

Interactive Integrated Exploration and Management of Visualization Parameters

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheVisualization 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.12

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

Visual Parameter Exploration in GPU Shader Space

The wide availability of high-performance GPUs has made the use of shader programs in visualization ubiquitous. Understanding shaders is a challenging task. Frequently it is difficult to mentally reconstruct the nature and types of transformations applied to the underlying data during the visualization process. We propose a method for the visual analysis of GPU shaders, which allows the flexible exploration and investigation of algorithms, parameters, and their effects. We introduce a method for extracting feature vectors composed of several attributes of the shader, as well as a direct manipulation interface for assigning semantics to them. The user interactively classifies pixels of images which are rendered with the investigated shader. The two resulting classes, a positive class and a negative one, are employed to steer the visualization. Based on this information, we can extract a wide variety of additional attributes and visualize their relation to this classification. Our system allows an interactive exploration of shader space and we demonstrate its utility for several different applications

Molecumentary: Scalable Narrated Documentaries Using Molecular Visualization

We present a method for producing documentary-style content using real-time scientific visualization. We produce molecumentaries, i.e., molecular documentaries featuring structural models from molecular biology. We employ scalable methods instead of the rigid traditional production pipeline. Our method is motivated by the rapid evolution of interactive scientific visualization, which shows great potential in science dissemination. Without some form of explanation or guidance, however, novices and lay-persons often find it difficult to gain insights from the visualization itself. We integrate such knowledge using the verbal channel and provide it along an engaging visual presentation. To realize the synthesis of a molecumentary, we provide technical solutions along two major production steps: 1) preparing a story structure and 2) turning the story into a concrete narrative. In the first step, information about the model from heterogeneous sources is compiled into a story graph. Local knowledge is combined with remote sources to complete the story graph and enrich the final result. In the second step, a narrative, i.e., story elements presented in sequence, is synthesized using the story graph. We present a method for traversing the story graph and generating a virtual tour, using automated camera and visualization transitions. Texts written by domain experts are turned into verbal representations using text-to-speech functionality and provided as a commentary. Using the described framework we synthesize automatic fly-throughs with descriptions that mimic a manually authored documentary. Furthermore, we demonstrate a second scenario: guiding the documentary narrative by a textual input

Molecumentary: Adaptable Narrated Documentaries Using Molecular Visualization

To appearInternational audienceWe present a method for producing documentary-style content using real-time scientific visualization. We introduce molecumentaries, i. e., molecular documentaries featuring structural models from molecular biology, created through adaptable methods instead of the rigid traditional production pipeline. Our work is motivated by the rapid evolution of scientific visualization and it potential in science dissemination. Without some form of explanation or guidance, however, novices and lay-persons often find it difficult to gain insights from the visualization itself. We integrate such knowledge using the verbal channel and provide it along an engaging visual presentation. To realize the synthesis of a molecumentary, we provide technical solutions along two major production steps: (1) preparing a story structure and (2) turning the story into a concrete narrative. In the first step, we compile information about the model from heterogeneous sources into a story graph. We combine local knowledge with external sources to complete the story graph and enrich the final result. In the second step, we synthesize a narrative, i. e., story elements presented in sequence, using the story graph. We then traverse the story graph and generate a virtual tour, using automated camera and visualization transitions. We turn texts written by domain experts into verbal representations using text-to-speech functionality and provide them as a commentary. Using the described framework, we synthesize fly-throughs with descriptions: automatic ones that mimic a manually authored documentary or semi-automatic ones which guide the documentary narrative solely through curated textual input