Person: Pfister, Hanspeter
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Pfister
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Hanspeter
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Pfister, Hanspeter
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Publication Genome-Wide Enhancer Maps Link Risk Variants to Disease Genes(Springer Science and Business Media LLC, 2021-04-07) Nasser, Joseph; Bergman, Drew T.; Fulco, Charles P.; Guckelberger, Philine; Doughty, Benjamin; Patwardhan, Tejal A.; Jones, Thouis; Nguyen, Tung; Ulirsch, Jacob; Lekschas, Fritz; Mualim, Kristy; Natri, Heini M.; Weeks, Elle M.; Munson, Glen; Kane, Michael; Kang, Helen Y.; Cui, Ang; Ray, John P.; Eisenhaure, Thomas M.; Collins, Ryan; Dey, Kushal; Pfister, Hanspeter; Price, Alkes; Epstein, Charles; Kundaje, Anshul; Xavier, Ramnik; Daly, Mark; Huang, Hailiang; Finucane, Hilary; Hacohen, Nir; Lander, Eric; Engreitz, JessePublication Display-aware image editing(IEEE, 2011) Jeong, Won-Ki; Johnson, Micah K.; Yu, Insu; Kautz, Jan; Pfister, Hanspeter; Paris, SylvainWe describe a set of image editing and viewing tools that explicitly take into account the resolution of the display on which the image is viewed. Our approach is twofold. First, we design editing tools that process only the visible data, which is useful for images larger than the display. This encompasses cases such as multi-image panoramas and high-resolution medical data. Second, we propose an adaptive way to set viewing parameters such brightness and contrast. Because we deal with very large images, different locations and scales often require different viewing parameters. We let users set these parameters at a few places and interpolate satisfying values everywhere else. We demonstrate the efficiency of our approach on different display and image sizes. Since the computational complexity to render a view depends on the display resolution and not the actual input image resolution, we achieve interactive image editing even on a 16 gigapixel image.Publication Characterizing Cancer Subtypes Using Dual Analysis in Caleydo StratomeX(Institute of Electrical and Electronics Engineers (IEEE), 2014-03) Turkay, Cagatay; Lex, Alexander; Streit, Marc; Pfister, Hanspeter; Hauser, HelwigIn this approach, dual-analysis views depict distributions of genes or data samples within Caleydo. Significant-difference plots show the elements of a cancer subtype that differ significantly from other subtypes. Analysts can characterize subtypes, investigate how samples relate to their subtype and other groups, and create well-defined subtypes based on statistical properties.Publication Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets(Institute of Electrical and Electronics Engineers (IEEE), 2009-11) Jeong, Won-Ki; Beyer, Johanna; Hadwiger, Markus; Vazquez-Reina, Amelio; Pfister, Hanspeter; Whitaker, RossRecent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes.Publication Exploring the Connectome: Petascale Volume Visualization of Microscopy Data Streams(Institute of Electrical & Electronics Engineers (IEEE), 2013) Beyer, Johanna; Hadwiger, Markus; Al-Awami, Ali; Jeong, Won-Ki; Kasthuri, Narayanan; Lichtman, Jeff; Pfister, HanspeterRecent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and dendrites as well as synapses have been segmented and labeled.Publication Example-based video color grading(Association for Computing Machinery (ACM), 2013) Bonneel, Nicolas; Sunkavalli, Kalyan; Paris, Sylvain; Pfister, HanspeterIn most professional cinema productions, the color palette of the movie is painstakingly adjusted by a team of skilled colorists -- through a process referred to as color grading -- to achieve a certain visual look. The time and expertise required to grade a video makes it difficult for amateurs to manipulate the colors of their own video clips. In this work, we present a method that allows a user to transfer the color palette of a model video clip to their own video sequence. We estimate a per-frame color transform that maps the color distributions in the input video sequence to that of the model video clip. Applying this transformation naively leads to artifacts such as bleeding and flickering. Instead, we propose a novel differential-geometry-based scheme that interpolates these transformations in a manner that minimizes their curvature, similarly to curvature flows. In addition, we automatically determine a set of keyframes that best represent this interpolated transformation curve, and can be used subsequently, to manually refine the color grade. We show how our method can successfully transfer color palettes between videos for a range of visual styles and a number of input video clips.Publication ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery(Institute of Electrical & Electronics Engineers (IEEE), 2014) Partl, Christian; Lex, Alexander; Streit, Marc; Strobelt, Hendrik; Wassermann, Anne-Mai; Pfister, Hanspeter; Schmalstieg, DieterLarge scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.Publication Local Layering for Joint Motion Estimation and Occlusion Detection(IEEE, 2014) Sun, Deqing; Liu, Ce; Pfister, HanspeterPublication NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity(Institute of Electrical & Electronics Engineers (IEEE), 2014) Al-Awami, Ali; Beyer, Johanna; Strobelt, Hendrik; Kasthuri, Narayanan; Lichtman, Jeff; Pfister, Hanspeter; Hadwiger, MarkusWe present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.Publication Reflectance and Illumination Video Editing using Fast User-Guided Intrinsic Decomposition(2014) Bonneel, Nicolas; Sun, Deqing; Sunkavalli, Kalyan; Paris, Sylvain; Pfister, HanspeterObject illumination and color are critical characteristics of a scene and being able to edit them allows artists to achieve powerful effects. Intrinsic image decomposition is the ideal component for this kind of tasks. By separating the illumination from the scene reflectance, it enables key operations such as recoloring and relighting. Significant progress has been done recently for decomposing static images. However, these algorithms rely on sophisticated optimization schemes that are computationally expensive and orders of magnitude too slow to be applied to video sequences. So much that even an optimized implementation would remain unpractical. In this paper, we introduce a user-guided algorithm that runs fast enough to be used in an interactive setting. Our strategy is to rely on an efficient sparse formulation – we also exploit the same kind of information as successful static methods but use it in ways that only have a minor impact on running time. The core of our approach is a gradient-domain `2-`p energy that models a sparse prior on reflectance gradients and a smooth prior on illumination. We show that the produced set of nonlinear equations can be solved very efficiently using look-up tables. Then, we provide scribbles to users to refine the decomposition. Our scribbles introduce local constraints in our optimization that add only a minimal overhead. Further, we extend these constraints to other similar image regions, thereby effectively enabling users to affect large regions with minimal effort. We also leverage multi-threading to precompute solutions a few frames ahead of the current one at a minimal cost. Coupled with the ability of our solver to use an initial guess to speed up convergence, this effectively shortens the computation time and offer a fast feedback to users. We demonstrate our approach on real sequences and show that we can obtain satisfying results with a reasonable amount of user interaction. We illustrate the benefits of our decomposition on video recoloring and shadow compositing.