A5017167333- Cell Image Analysis Techniques
- Morphological variations and asymmetry
- Image Retrieval and Classification Techniques
- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
- Single-cell and spatial transcriptomics
- Particle physics theoretical and experimental studies
- Classical Antiquity Studies
- Renaissance Literature and Culture
- Biblical Studies and Interpretation
- Advanced Fluorescence Microscopy Techniques
- Image Processing and 3D Reconstruction
- Pleistocene-Era Hominins and Archaeology
- Reformation and Early Modern Christianity
- Tensor decomposition and applications
- Diamond and Carbon-based Materials Research
- Latin American history and culture
- Human Pose and Action Recognition
- Radiation Detection and Scintillator Technologies
- Robotics and Sensor-Based Localization
- Video Analysis and Summarization
- Digital Image Processing Techniques
- History and Developments in Astronomy
- Advanced Image Processing Techniques
- Topological and Geometric Data Analysis
Rite-Solutions (United States)
2019
Czech Academy of Sciences, Institute of Computer Science
2011-2016
University of Bonn
2009-2016
Johannes Gutenberg University Mainz
2009
Despite the widely recognized importance of symmetric second order tensor fields in medicine and engineering, visualization data uncertainty is still its infancy. A recently proposed tensorial normal distribution, involving a fourth covariance tensor, provides mathematical description how different aspects field, such as trace, anisotropy, or orientation, vary covary at each point. However, this wealth information far too rich for human analyst to take single glance, no suitable tools are...
The longitudinal polarisation transfer from muons to lambda and anti-lambda hyperons, D_LL, has been studied in deep inelastic scattering off an unpolarised isoscalar target at the COMPASS experiment CERN. spin transfers produced current fragmentation region exhibit different behaviours as a function of x xF . measured dependences D^lambda_LL are compatible with zero, while D^anti-lambda_LL tends increase xF, reaching values 0.4 - 0.5. resulting average = -0.012 +- 0.047 0.024 0.249 0.056...
Large image deformations pose a challenging problem for the visualization and statistical analysis of 3D ensembles which have multitude applications in biology medicine. Simple linear interpolation tangent space ensemble introduces artifactual anatomical structures that hamper application targeted visual shape techniques. In this work we make use theory stationary velocity fields to facilitate interactive non-linear plausible extrapolation high quality rendering large devise an efficient...
Gaining insight into anatomic co variation helps the understanding of organismic shape variability in general and is particular interest for delimiting morphological modules. Generation hypotheses on structural undoubtedly a highly creative process, as such, requires an exploratory approach. In this work we propose new local covariance tensor which enables interactive visualizations to explore at different levels detail, stimulating rapid formation (qualitative) evaluation hypotheses. The...
Creating geometrically detailed mesh animations is an involved and resource-intensive process in digital content creation. In this work we present a method to rapidly combine available sparse motion capture data with existing sequences produce large variety of new animations. The key idea model shape changes correlated the pose animated object via part-based statistical model. We observe that compact linear models suffice for segmentation into nearly rigid parts. same further guides...
A common technique in 3D shape analysis is to describe variability using a statistical deformation model (SDM). In contrast the use of sparse landmark data for volume this SDM based on dense registrations input shapes. For valuable exploration space setting biological morphometrics we identified two prominent objectives visual investigation. The first objective detect possible variations between anatomically different groups individuals. second integrate and exploit expert knowledge about...
Abstract The use of spatially varying reflectance models (SVBRDF) is the state art in physically based rendering and ultimate goal to acquire them from real world samples. Recently several promising deep learning approaches have emerged that create such a few uncalibrated photos, after being trained on synthetic SVBRDF datasets. While achieved results are already very impressive, reconstruction accuracy by these still far specialized devices. On other hand, fitting parameter maps gibabytes...
A recurrent problem in biological image analysis is to quantify the number and size of spots on a homogeneous background. Most automated approaches rely segmenting individual spots, which becomes unreliable when contains artifacts, noise, or confounding objects. Therefore, practitioners often resort tedious time-consuming manual counting measurements. As an alternative, we propose visual analytics approach this problem. It based Total Variation Flow, partial differential equation that...
A recurrent problem in the analysis of microscopy images is to quantify number and size spots on a homogeneous background. Unfortunately, segmenting individual becomes unreliable when they are close together, or image contains noise artifacts. On other hand, manual counting line-scan measurements prone bias too time-consuming be used high-throughput microscopy. In this work, we derive novel per-pixel measures spot scale density from Total Variation Flow, partial differential equation that...
High performance calorimetry conducted at future hadron colliders, such as the FCC-hh, poses a significant challenge for applying current detector technologies due to unprecedented beam luminosities and radiation fields. Solutions include developing scintillators that are capable of separating events sub-fifty picosecond level while also maintaining after extreme constant neutron ionizing exposure. The RADiCAL is an approach incorporates tolerant materials in sampling 'shashlik' style...