006 Spezielle Computerverfahren
Refine
H-BRS Bibliography
- yes (85) (remove)
Departments, institutes and facilities
- Fachbereich Informatik (60)
- Institute of Visual Computing (IVC) (29)
- Fachbereich Wirtschaftswissenschaften (17)
- Institut für Verbraucherinformatik (IVI) (14)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (12)
- Institut für Sicherheitsforschung (ISF) (8)
- Fachbereich Ingenieurwissenschaften und Kommunikation (5)
- Graduierteninstitut (3)
- Institut für KI und Autonome Systeme (A2S) (2)
- Zentrum für Ethik und Verantwortung (ZEV) (2)
Document Type
- Conference Object (36)
- Article (26)
- Preprint (5)
- Report (5)
- Part of a Book (4)
- Contribution to a Periodical (4)
- Doctoral Thesis (3)
- Book (monograph, edited volume) (1)
- Research Data (1)
Year of publication
Keywords
- Augmented Reality (5)
- Machine Learning (4)
- Knowledge Graphs (3)
- Virtual Reality (3)
- haptics (3)
- virtual reality (3)
- 3D user interface (2)
- Bioinformatics (2)
- Natural Language Processing (2)
- Ray tracing (2)
- Robotics (2)
- Transformers (2)
- authoring tools (2)
- guidance (2)
- mixed reality (2)
- prototyping (2)
- 3D navigation (1)
- 450 MHz (1)
- AI usage in sports (1)
- AR (1)
- AR design (1)
- AR development (1)
- AR/VR (1)
- Agile software development (1)
- Algorithmik (1)
- Altenhilfe (1)
- Aneignungsstudie (1)
- Applications in Energy Transport (1)
- Artificial Intelligence (1)
- Auditory Cueing (1)
- Automatic Differentiation (1)
- Ball Tracking (1)
- Bayesian Deep Learning (1)
- Behaviour-Driven Development (1)
- Blasendiagramm (1)
- Business Process Intelligence (1)
- Camera selection (1)
- Camera view analysis (1)
- Case study (1)
- Classifiers (1)
- Codes (1)
- Collaborating industrial robots (1)
- Community of Practice (1)
- Complex Systems Modeling and Simulation (1)
- Complexity (1)
- Compliant fingers (1)
- Computational fluid dynamics (1)
- Computergrafik (1)
- Concurrent repeated failure prognosis (1)
- Conformation (1)
- Crossmedia (1)
- Crystal structure (1)
- Current research information systems (1)
- Curriculum (1)
- Cybersickness (1)
- Data Fusion (1)
- Data structures (1)
- Datenanalyse (1)
- Dementia (1)
- Demenz (1)
- Demonstration-based training (1)
- Design (1)
- Design Recommendations (1)
- Design Theory and Practice (1)
- Diagnostic bond graph-based online fault diagnosis (1)
- Disco (1)
- Distance Perception (1)
- Drosophila (1)
- Educational Data Mining (1)
- Educational Process Mining (1)
- Embedded system (1)
- Emotion (1)
- Entropy (1)
- Exergame (1)
- Experten (1)
- Facial Emotion Recognition (1)
- Fallbeschreibung (1)
- Feedback (1)
- Flow control (1)
- Fluency (1)
- Forests (1)
- Functional safety (1)
- Fuzzy Mining (1)
- Games and Simulations for Learning (1)
- Geometry (1)
- Geschäftsprozess (1)
- Graph embeddings (1)
- Graph theory (1)
- Guidelines (1)
- HCI (1)
- HDBR (1)
- Hardware (1)
- Head-mounted Display (1)
- Higher education (1)
- Human factors (1)
- Human orientation perception (1)
- Human-Food-Interaction (1)
- Hyperspectral image (1)
- ICT (1)
- IEC 104 (1)
- IEC 61850 (1)
- Increasing fault magnitude (1)
- Inductive Logic Programming (1)
- Inductive Visual Mining (1)
- Information Security (1)
- Instruction design (1)
- Intermittent faults (1)
- Kinect (1)
- Kollektiventscheidung (1)
- Komplexitätstheorie (1)
- LTE-M (1)
- Language learning (1)
- Langzeitbehandlung (1)
- Lattice Boltzmann Method (1)
- Ligands (1)
- Living Lab (1)
- Locomotion (1)
- MQTT (1)
- MR (1)
- Mathematical methods (1)
- Microgravity (1)
- Mixed Reality (1)
- Model-driven engineering (1)
- Molecular structure (1)
- Motion Sickness (1)
- Multi-camera (1)
- NIR-point sensor (1)
- NLP (1)
- Navigation (1)
- Negotiation of Taste (1)
- Neural representations (1)
- Neuroscience (1)
- Non-linear systems (1)
- OCT (1)
- Object-Based Image Analysis (OBIA) (1)
- Optical Flow (1)
- Out-of-view Objects (1)
- PAD (1)
- Perception (1)
- Perceptual Upright (1)
- Pflegepersonal (1)
- ProM (1)
- Process Mining (1)
- Pronunciation (1)
- Proximity (1)
- Psychology (1)
- Pytorch (1)
- Qualitative study (1)
- Raman microscopy (1)
- RapidMiner (1)
- Real-Time Image Processing (1)
- Reasoning (1)
- Recommender systems (1)
- Remaining Useful Life (RUL) estimates (1)
- Requirements (1)
- Review (1)
- Robust grasping (1)
- SMPA loop (1)
- Semantic search (1)
- Serious Games (1)
- Skin detection (1)
- Slippage detection (1)
- Smart Grid (1)
- Smart Home (1)
- Smart InGaAs camera-system (1)
- Social-Choice-Theorie (1)
- Spherical Treadmill (1)
- Spieltheorie (1)
- Studenten (1)
- Studienverlauf (1)
- Survey (1)
- Taste (1)
- Technologie (1)
- Three-dimensional displays (1)
- Topology (1)
- Traffic Simulations (1)
- Travel Techniques (1)
- Tree Stumps (1)
- UAV (1)
- Ultrasonic array (1)
- Uncertainty Quantification (1)
- Underwater (1)
- Unmanned Aerial Vehicle (UAV) (1)
- Unterstützung (1)
- User Experience (1)
- User Interface Design (1)
- User centered design (1)
- User feedback (1)
- User-Centered Design (1)
- VR (1)
- Videogame (1)
- View selection (1)
- Virtual Agents (1)
- Virtuelle Realität (1)
- Visual Cueing (1)
- Visual Discrimination (1)
- Visuelle Wahrnehmung (1)
- Vulnerable Groups (1)
- Wissensaustausch (1)
- XR (1)
- adaptive trigger (1)
- aerodynamics (1)
- audio-tactile feedback (1)
- augmented reality (1)
- authentication (1)
- authoring (1)
- biometrics (1)
- brightfield microscopy (1)
- co-design (1)
- collision (1)
- component analyses (1)
- computer vision (1)
- controller design (1)
- depth perception (1)
- dynamic vector fields (1)
- elite sports (1)
- explainable AI (1)
- fingerprint (1)
- fitness-fatigue model (1)
- flight zone (1)
- geofence (1)
- head down bed rest (1)
- image fusion (1)
- interaction design (1)
- interactive computer graphics (1)
- interface design (1)
- leaning-based interfaces (1)
- locomotion interface (1)
- mathematical modeling (1)
- multisensory (1)
- navigational search (1)
- neural networks (1)
- neutral buoyancy (1)
- optic flow (1)
- optical coherence tomography (1)
- pansharpening (1)
- path tracing (1)
- performance modeling (1)
- performance prediction (1)
- practitioners (1)
- presentation attack detection (1)
- psychophysics (1)
- real-time (1)
- remote sensing (1)
- self-motion perception (1)
- sensory perception (1)
- space flight analog (1)
- spatial orientation (1)
- spatial updating (1)
- subjective visual vertical (1)
- training performance relationship (1)
- vection (1)
- vibration (1)
- virtual reality, XR (1)
- weight perception (1)
Wie KI Innere Führung lernt
(2022)
Dass sich künstliche Intelligenz (KI) weltweit ausgebreitet hat, ist eine Binsenwahrheit. Die rasche und unaufhaltsame Proliferation von KI der letzten zehn Jahre spricht für sich, und längst ziehen auch Gesetzgeber und Regulierungsbehörden nach, um KI und ihre Technikfolgen einzuhegen. Für Deutschland relevante Gestaltungsanforderungen haben die High-Level Expert Group on Artificial Intelligence der Europäischen Kommission (HLEG AI) und auf nationaler Ebene die Datenethikkommission der Bundesregierung (DEK) und die Enquetekommission Künstliche Intelligenz des Deutschen Bundestags (EKKI) geäußert.
Background: Virtual reality combined with spherical treadmills is used across species for studying neural circuits underlying navigation.
New Method: We developed an optical flow-based method for tracking treadmil ball motion in real-time using a single high-resolution camera.
Results: Tracking accuracy and timing were determined using calibration data. Ball tracking was performed at 500 Hz and integrated with an open source game engine for virtual reality projection. The projection was updated at 120 Hz with a latency with respect to ball motion of 30 ± 8 ms.
Comparison: with Existing Method(s) Optical flow based tracking of treadmill motion is typically achieved using optical mice. The camera-based optical flow tracking system developed here is based on off-the-shelf components and offers control over the image acquisition and processing parameters. This results in flexibility with respect to tracking conditions – such as ball surface texture, lighting conditions, or ball size – as well as camera alignment and calibration.
Conclusions: A fast system for rotational ball motion tracking suitable for virtual reality animal behavior across different scales was developed and characterized.
While humans can effortlessly pick a view from multiple streams, automatically choosing the best view is a challenge. Choosing the best view from multi-camera streams poses a problem regarding which objective metrics should be considered. Existing works on view selection lack consensus about which metrics should be considered to select the best view. The literature on view selection describes diverse possible metrics. And strategies such as information-theoretic, instructional design, or aesthetics-motivated fail to incorporate all approaches. In this work, we postulate a strategy incorporating information-theoretic and instructional design-based objective metrics to select the best view from a set of views. Traditionally, information-theoretic measures have been used to find the goodness of a view, such as in 3D rendering. We adapted a similar measure known as the viewpoint entropy for real-world 2D images. Additionally, we incorporated similarity penalization to get a more accurate measure of the entropy of a view, which is one of the metrics for the best view selection. Since the choice of the best view is domain-dependent, we chose demonstration-based training scenarios as our use case. The limitation of our chosen scenarios is that they do not include collaborative training and solely feature a single trainer. To incorporate instructional design considerations, we included the trainer’s body pose, face, face when instructing, and hands visibility as metrics. To incorporate domain knowledge we included predetermined regions’ visibility as another metric. All of those metrics are taken into account to produce a parameterized view recommendation approach for demonstration-based training. An online study using recorded multi-camera video streams from a simulation environment was used to validate those metrics. Furthermore, the responses from the online study were used to optimize the view recommendation performance with a normalized discounted cumulative gain (NDCG) value of 0.912, which shows good performance with respect to matching user choices.
Neutral buoyancy has been used as an analog for microgravity from the earliest days of human spaceflight. Compared to other options on Earth, neutral buoyancy is relatively inexpensive and presents little danger to astronauts while simulating some aspects of microgravity. Neutral buoyancy removes somatosensory cues to the direction of gravity but leaves vestibular cues intact. Removal of both somatosensory and direction of gravity cues while floating in microgravity or using virtual reality to establish conflicts between them has been shown to affect the perception of distance traveled in response to visual motion (vection) and the perception of distance. Does removal of somatosensory cues alone by neutral buoyancy similarly impact these perceptions? During neutral buoyancy we found no significant difference in either perceived distance traveled nor perceived size relative to Earth-normal conditions. This contrasts with differences in linear vection reported between short- and long-duration microgravity and Earth-normal conditions. These results indicate that neutral buoyancy is not an effective analog for microgravity for these perceptual effects.
Vection underwater
(2022)
Using Visual and Auditory Cues to Locate Out-of-View Objects in Head-Mounted Augmented Reality
(2021)
„Industrie 4.0“ und weitere Schlagwörter wie „Big Data“, „Internet der Dinge“ oder „Cyber-physical Systems“ werden gegenwärtig in der Wirtschaft häufig aufgegriffen. Ausgangspunkt hierfür ist die Vernetzung von IT-Technologien sowie die durchgängige Digitalisierung. Nicht nur die Geschäftsfelder und Business-Modelle der Unternehmen selbst unterliegen dabei ei-nem entsprechend radikalen Wandel, dieser bezieht sich auch auf die Arbeitsumgebungen der Mitarbeiter, sowie den privaten und den öffentlichen Raum (Botthof, 2015; Hartmann, 2015).
It is challenging to provide users with a haptic weight sensation of virtual objects in VR since current consumer VR controllers and software-based approaches such as pseudo-haptics cannot render appropriate haptic stimuli. To overcome these limitations, we developed a haptic VR controller named Triggermuscle that adjusts its trigger resistance according to the weight of a virtual object. Therefore, users need to adapt their index finger force to grab objects of different virtual weights. Dynamic and continuous adjustment is enabled by a spring mechanism inside the casing of an HTC Vive controller. In two user studies, we explored the effect on weight perception and found large differences between participants for sensing change in trigger resistance and thus for discriminating virtual weights. The variations were easily distinguished and associated with weight by some participants while others did not notice them at all. We discuss possible limitations, confounding factors, how to overcome them in future research and the pros and cons of this novel technology.
Self-motion perception is a multi-sensory process that involves visual, vestibular, and other cues. When perception of self-motion is induced using only visual motion, vestibular cues indicate that the body remains stationary, which may bias an observer’s perception. When lowering the precision of the vestibular cue by for example, lying down or by adapting to microgravity, these biases may decrease, accompanied by a decrease in precision. To test this hypothesis, we used a move-to-target task in virtual reality. Astronauts and Earth-based controls were shown a target at a range of simulated distances. After the target disappeared, forward self-motion was induced by optic flow. Participants indicated when they thought they had arrived at the target’s previously seen location. Astronauts completed the task on Earth (supine and sitting upright) prior to space travel, early and late in space, and early and late after landing. Controls completed the experiment on Earth using a similar regime with a supine posture used to simulate being in space. While variability was similar across all conditions, the supine posture led to significantly higher gains (target distance/perceived travel distance) than the sitting posture for the astronauts pre-flight and early post-flight but not late post-flight. No difference was detected between the astronauts’ performance on Earth and onboard the ISS, indicating that judgments of traveled distance were largely unaffected by long-term exposure to microgravity. Overall, this constitutes mixed evidence as to whether non-visual cues to travel distance are integrated with relevant visual cues when self-motion is simulated using optic flow alone.
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs. This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against two baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.083. Additionally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. Finally, the source code and pre-trained STonKGs models are available at https://github.com/stonkgs/stonkgs and https://huggingface.co/stonkgs/stonkgs-150k.
MOTIVATION
The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited.
RESULTS
To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e., from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications.
AVAILABILITY
We make the source code and the Python package of STonKGs available at GitHub (https://github.com/stonkgs/stonkgs) and PyPI (https://pypi.org/project/stonkgs/). The pre-trained STonKGs models and the task-specific classification models are respectively available at https://huggingface.co/stonkgs/stonkgs-150k and https://zenodo.org/communities/stonkgs.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Die Entwicklung intelligenter Technologien zur Unterstützung im Alltag und in den eigenen vier Wänden begleitet unsere Gesellschaft schon seit dem Zeitalter des Personal Computers. Mit dem Aufkommen des Internet der Dinge und begünstigt durch immer kleiner und günstiger werdende Hardware ergeben sich neue Potenziale, die das Thema Smart Home attraktiver als je zuvor werden lassen. Eine Vielzahl der aktuell im Markt verfügbaren Lösungen adressiert die Bedürfnisse Komfort, Sicherheit und effiziente Energienutzung. Die versprochene Intelligenz – smartness, wie sie der Begriff selbst suggeriert – wird vor allem bei Lösungen im privaten Nachrüstbereich überwiegend durch die Interaktion der Nutzer selbst und entsprechende regelbasierte Konfigurationen erzeugt. Diese notwendige Art der Interaktion und die damit verbundenen Aufwände sind jedoch von starker Bedeutung für das gesamte Nutzungserlebnis Smart Home und führen nicht selten zu Frustration oder gar Resignation in der Nutzung.
Selection Performance and Reliability of Eye and Head Gaze Tracking Under Varying Light Conditions
(2024)
Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data
(2022)
Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.