DSpace Repository :: Browsing by Author "Pfister, Hanspeter"

Browsing by Author "Pfister, Hanspeter"

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ARrow: A Real-Time AR Rowing Coach
(The Eurographics Association, 2023) Iannucci, Elena; Chen, Zhutian; Armeni, Iro; Pollefeys, Marc; Pfister, Hanspeter; Beyer, Johanna; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
Rowing requires physical strength and endurance in athletes as well as a precise rowing technique. The ideal rowing stroke is based on biomechanical principles and typically takes years to master. Except for time-consuming video analysis after practice, coaches currently have no means to quantitatively analyze a rower's stroke sequence and body movement. We propose ARrow, an AR application for coaches and athletes that provides real-time and situated feedback on a rower's body position and stroke. We use computer vision techniques to extract the rower's 3D skeleton and to detect the rower's stroke cycle. ARrow provides visual feedback on three levels: Tracking of basic performance metrics over time, visual feedback and guidance on a rower's stroke sequence, and a rowing ghost view that helps synchronize the body movement of two rowers. We developed ARrow in close colaboration with international rowing coaches and demonstrate its usefulness in a user study with athletes and coaches.
Barrio: Customizable Spatial Neighborhood Analysis and Comparison for Nanoscale Brain Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Troidl, Jakob; Cali, Corrado; Gröller, Eduard; Pfister, Hanspeter; Hadwiger, Markus; Beyer, Johanna; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
High-resolution electron microscopy imaging allows neuroscientists to reconstruct not just entire cells but individual cell substructures (i.e., cell organelles) as well. Based on these data, scientists hope to get a better understanding of brain function and development through detailed analysis of local organelle neighborhoods. In-depth analyses require efficient and scalable comparison of a varying number of cell organelles, ranging from two to hundreds of local spatial neighborhoods. Scientists need to be able to analyze the 3D morphologies of organelles, their spatial distributions and distances, and their spatial correlations. We have designed Barrio as a configurable framework that scientists can adjust to their preferred workflow, visualizations, and supported user interactions for their specific tasks and domain questions. Furthermore, Barrio provides a scalable comparative visualization approach for spatial neighborhoods that automatically adjusts visualizations based on the number of structures to be compared. Barrio supports small multiples of spatial 3D views as well as abstract quantitative views, and arranges them in linked and juxtaposed views. To adapt to new domain-specific analysis scenarios, we allow the definition of individualized visualizations and their parameters for each analysis session. We present an in-depth case study for mitochondria analysis in neuronal tissue and demonstrate the usefulness of Barrio in a qualitative user study with neuroscientists.
Bird's-Eye - Large-Scale Visual Analytics of City Dynamics using Social Location Data
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Krueger, Robert; Han, Qi; Ivanov, Nikolay; Mahtal, Sanae; Thom, Dennis; Pfister, Hanspeter; Ertl, Thomas; Gleicher, Michael and Viola, Ivan and Leitte, Heike
The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large-scale and up-to-date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up-to-date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal-and-task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back-end and an interactive visual analytics interface. The monitoring component uses intelligent pre-aggregation to allow dynamic queries in near real-time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research.
PEAX: Interactive Visual Pattern Search in Sequential Data Using Unsupervised Deep Representation Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Lekschas, Fritz; Peterson, Brant; Haehn, Daniel; Ma, Eric; Gehlenborg, Nils; Pfister, Hanspeter; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
We present PEAX, a novel feature-based technique for interactive visual pattern search in sequential data, like time series or data mapped to a genome sequence. Visually searching for patterns by similarity is often challenging because of the large search space, the visual complexity of patterns, and the user's perception of similarity. For example, in genomics, researchers try to link patterns in multivariate sequential data to cellular or pathogenic processes, but a lack of ground truth and high variance makes automatic pattern detection unreliable. We have developed a convolutional autoencoder for unsupervised representation learning of regions in sequential data that can capture more visual details of complex patterns compared to existing similarity measures. Using this learned representation as features of the sequential data, our accompanying visual query system enables interactive feedback-driven adjustments of the pattern search to adapt to the users' perceived similarity. Using an active learning sampling strategy, PEAX collects user-generated binary relevance feedback. This feedback is used to train a model for binary classification, to ultimately find other regions that exhibit patterns similar to the search target. We demonstrate PEAX's features through a case study in genomics and report on a user study with eight domain experts to assess the usability and usefulness of PEAX. Moreover, we evaluate the effectiveness of the learned feature representation for visual similarity search in two additional user studies. We find that our models retrieve significantly more similar patterns than other commonly used techniques.
Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach
(The Eurographics Association, 2023) Harth, Philipp; Bast, Arco; Troidl, Jakob; Meulemeester, Bjorge; Pfister, Hanspeter; Beyer, Johanna; Oberlaender, Marcel; Hege, Hans-Christian; Baum, Daniel; Hansen, Christian; Procter, James; Renata G. Raidou; Jönsson, Daniel; Höllt, Thomas
Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research.
A Survey of Visualization and Analysis in High-Resolution Connectomics
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Beyer, Johanna; Troidl, Jakob; Boorboor, Saeed; Hadwiger, Markus; Kaufman, Arie; Pfister, Hanspeter; Bruckner, Stefan; Turkay, Cagatay; Vrotsou, Katerina
The field of connectomics aims to reconstruct the wiring diagram of neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the neuronal connectivity, however, relies on many individual steps, starting from high-resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state-of-the-art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open-source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.
VICE: Visual Identification and Correction of Neural Circuit Errors
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Gonda, Felix; Wang, Xueying; Beyer, Johanna; Hadwiger, Markus; Lichtman, Jeff W.; Pfister, Hanspeter; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease. Recent advances in automatic image segmentation and synapse prediction in electron microscopy (EM) datasets of the brain have made reconstructions of neurons possible at the nanometer scale. However, automatic segmentation sometimes struggles to segment large neurons correctly, requiring human effort to proofread its output. General proofreading involves inspecting large volumes to correct segmentation errors at the pixel level, a visually intensive and time-consuming process. This paper presents the design and implementation of an analytics framework that streamlines proofreading, focusing on connectivity-related errors. We accomplish this with automated likely-error detection and synapse clustering that drives the proofreading effort with highly interactive 3D visualizations. In particular, our strategy centers on proofreading the local circuit of a single cell to ensure a basic level of completeness. We demonstrate our framework's utility with a user study and report quantitative and subjective feedback from our users. Overall, users find the framework more efficient for proofreading, understanding evolving graphs, and sharing error correction strategies.
Visualizing and Interacting with Geospatial Networks: A Survey and Design Space
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Schöttler, Sarah; Yang, Yalong; Pfister, Hanspeter; Bach, Benjamin; Benes, Bedrich and Hauser, Helwig
This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks. Visualizing geospatial networks poses numerous challenges around the integration of both network and geographical information as well as additional information such as node and link attributes, time and uncertainty. Our overview analyses existing techniques along four dimensions: (i) the representation of geographical information, (ii) the representation of network information, (iii) the visual integration of both and (iv) the use of interaction. These four dimensions allow us to discuss techniques with respect to the trade‐offs they make between showing information across all these dimensions and how they solve the problem of showing as much information as necessary while maintaining readability of the visualization. .

Browsing by Author "Pfister, Hanspeter"

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