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Towards Neuroadaptive Augmented Reality Piano Tutorials

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Towards Neuroadaptive Augmented Reality Piano Tutorials

Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Florian Maitz Lucchas Ribeiro Skreinig Denis Kalkofen Selina C. Wriessnegger Institute of Neural Engineering Graz University of Technology Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

EEG-based neuro-adaptive systems are becoming increasingly popular in the field of learning and education. Their ability to detect changes in brain activity in real-time makes them unique and enables monitoring mental workload (MWL) and fatigue. We present a neuro-adaptive system that combines an Augmented Reality (AR) piano tutorial with online EEG measurements of its user, delivered by a passive Brain computer Interface (BCI) system. The MWL was measured by means of EEG and a Filter Bank Common Spatial Patter algorithm (FBCSP) was trained to differentiate between low and high levels of MWL. Low levels were connected to the 0-back task and high levels to the 2-back task. The n-back task was a calibration task to train a binary Machine Learning (ML) classifier to differentiate between low and high MWL. This trained ML algorithm was then used as the central element of the passive BCI, which constantly classified small windows of the EEG during the piano tutorial and adapted the difficulty of the tutorial. 22 Participants were randomly separated into two groups: adaptive and non-adaptive piano tutorial. The results of the non-adaptive group showed significantly higher levels of classified MWL throughout the piano tutorial.

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Identifying and Extracting Pedestrian Behavior in Critical Traffic Situations

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Identifying and Extracting Pedestrian Behavior in Critical T...

International Conference on Intelligent Transportation

作者： Martin Schachner Bernd Schneider Fabian Weissenbacher Nadezda Kirillova Horst Possegger Horst Bischof Corina Klug Vehicle Safety Institute Graz University of Technology Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

A better understanding of interactive pedestrian behavior in critical traffic situations is essential for the development of enhanced pedestrian safety systems. Real-world traffic observations play a decisive role in this, since they represent behavior in an unbiased way. In this work, we present an approach of how a subset of very considerable pedestrian-vehicle interactions can be derived from a camerabased observation system. For this purpose, we have examined road user trajectories automatically for establishing temporal and spatial relationships, using 110h hours of video recordings. In order to identify critical interactions, our approach combines the metric post-encroachment time with a newly introduced motion adaption metric. From more than 11,000 reconstructed pedestrian trajectories, 259 potential scenarios remained, using a post-encroachment time threshold of 2s. However, in 95% of cases, no adaptation of the pedestrian behavior was observed due to avoiding criticality. Applying the proposed motion adaption metric, only 21 critical scenarios remained. Manual investigations revealed that critical pedestrian vehicle interactions were present in 7 of those. They were further analyzed and made publicly available for developing pedestrian behavior models 3 3 https://***/10.3217/xytxf-kjn62. The results indicate that critical interactions in which the pedestrian perceives and reacts to the vehicle at a relatively late stage can be extracted using the proposed method.

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GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and Light-weight Point Set Registration Algorithm

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arXiv 2023年

作者： Mohr, Ludwig Geles, Ismail Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria

Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://***/mordecaimalignatius/GAFAR/. Copyright © 2023, The Authors. All rights reserved.

关键词： computer vision

Deep Learning-Based Point Cloud Registration for Augmented Reality-Guided Surgery

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Deep Learning-Based Point Cloud Registration for Augmented R...

IEEE International Symposium on Biomedical Imaging

作者： Maximilian Weber Daniel Wild Jens Kleesiek Jan Egger Christina Gsaxner Institute of Computer Graphics and Vision Graz University of Technology Institute for Artificial Intelligence in Medicine University Hospital Essen (AöR)

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Point cloud registration aligns 3D point clouds using spatial transformations. It is an important task in computer vision, with applications in areas such as augmented reality (AR) and medical imaging. This work explores the intersection of two research trends: the integration of AR into image-guided surgery and the use of deep learning for point cloud registration. The main objective is to evaluate the feasibility of applying deep learning-based point cloud registration methods for image-to-patient registration in augmented reality-guided surgery. We created a dataset of point clouds from medical imaging and corresponding point clouds captured with a popular AR device, the HoloLens 2. We evaluate three well-established deep learning models in registering these data pairs. While we find that some deep learning methods show promise, we show that a conventional registration pipeline still outperforms them on our challenging dataset.

关键词： Point cloud compression Deep learning Accuracy Three-dimensional displays Noise Surgery Registers

Multicenter aortic vessel tree extraction using deep learning

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Multicenter aortic vessel tree extraction using deep learnin...

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Scharinger, Bernhard Pepe, Antonio Jin, Yuan Gsaxner, Christina Li, Jianning Egger, Jan Graz University of Technology Institute for Computer Graphics and Vision Inffeldgasse 16c/II GrazA-8010 Austria Computer Algorithms for Medicine Laboratory Graz Austria Girardetstrasse 2 Essen45131 Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou311121 China

ISBN: (纸本)9781510660410

The aorta is the largest vessel of the human body and its pathological degenerations, such as dissections and aneurysms, can be life threatening. An automatic and fast segmentation of the aorta can therefore be a helpful tool to quickly identify an abnormal anatomy. The segmentation of the aortic vessel tree (AVT) typically requires extensive manual labor, but, in recent years, progress in deep learning techniques made the automation of this process viable. For this purpose, we tested different deep learning networks to segment the aortic vessel tree from computed tomography angiography (CTA) scans with a deep neural network consisting of an encoder-decoder architecture with skip connections and an optional self-attention block. The networks were trained on a dataset of 56 CTA scans from three different sources and resulted in Dice score similarities between 0.043-0.897. Generally, the classical U-Nets performed better than the ones containing a self-attention block, indicating that they might diminish performance for AVT segmentation. The quality of the resulting segmentations was highly dependent on the CTA image quality, especially on the contrast between the aorta and the surrounding tissues. However, the trained deep neural network can segment CTA scans well with limited computational resources and training data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Deep neural networks

Free-Viewpoint Visual Inspection via 3D Gaussian Splatting for Direct Template Matching

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Free-Viewpoint Visual Inspection via 3D Gaussian Splatting f...

Annual Conference of Industrial Electronics Society

作者： Kenta Ito Shiori Ueda Shohei Mori Junichi Sugano Hideyuki Adachi Hideo Saito Information and Computer Science Keio University Yokohama Japan Institute of Computer Graphics and Vision Graz University of Technology Graz Austria ViSCO Technologies Corporation Tokyo Japan

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Machine vision systems play a pivotal role in streamlining manufacturing processes, notably in quality control through automatic in-line visual inspections. A common practice for inspecting parts, components, and final products is to use a master part benchmark for quality comparison. However, challenges arise when objects enter inspection points in unintended orientations. This misalignment potentially leads to erroneous decisions by automated systems, resulting in additional checkpoints or wastage affecting the production rate. To tackle this issue, we propose a visual inspection pipeline that leverages recent machine learning-based approaches to compare the inspection target and a master part virtually oriented to the same perspective. Specifically, we suggest combining 3D Gaussian Splatting and DUSt3R as a practical solution. Our approach demonstrates its efficacy in real-world scenarios through testing on three mock parts and a real industrial component.

关键词： Visualization Three-dimensional displays Manufacturing processes Machine vision Pipelines Pose estimation Prototypes Production Quality control Inspection

Diffusion-based generation of Histopathological Whole Slide Images at a Gigapixel scale

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arXiv 2023年

作者： Harb, Robert Pock, Thomas Müller, Heimo Institute of Computer Graphics and Vision Graz University of Technology Austria Diagnostic and Research Institute of Pathology Medical University of Graz Austria

We present a novel diffusion-based approach to generate synthetic histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel scale. Synthetic WSIs have many potential applications: They can augment training datasets to enhance the performance of many computational pathology applications. They allow the creation of synthesized copies of datasets that can be shared without violating privacy regulations. Or they can facilitate learning representations of WSIs without requiring data annotations. Despite this variety of applications, no existing deep-learning-based method generates WSIs at their typically high resolutions. Mainly due to the high computational complexity. Therefore, we propose a novel coarse-to-fine sampling scheme to tackle image generation of high-resolution WSIs. In this scheme, we increase the resolution of an initial low-resolution image to a high-resolution WSI. Particularly, a diffusion model sequentially adds fine details to images and increases their resolution. In our experiments, we train our method with WSIs from the TCGA-BRCA dataset. Additionally to quantitative evaluations, we also performed a user study with pathologists. The study results suggest that our generated WSIs resemble the structure of real WSIs. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

NeuRSS: Enhancing AUV Localization and Bathymetric Mapping with Neural Rendering for Sidescan SLAM

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arXiv 2024年

作者： Xie, Yiping Zhang, Jun Bore, Nils Folkesson, John Robotics Perception and Learning Division KTH Royal Institute of Technology Stockholm Sweden The Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Ocean Infinity United States

Implicit neural representations and neural rendering have gained increasing attention for bathymetry estimation from sidescan sonar (SSS). These methods incorporate multiple observations of the same place from SSS data to constrain the elevation estimate, converging to a globally-consistent bathymetric model. However, the quality and precision of the bathymetric estimate are limited by the positioning accuracy of the autonomous underwater vehicle (AUV) equipped with the sonar. The global positioning estimate of the AUV relying on dead reckoning (DR) has an unbounded error due to the absence of a geo-reference system like GPS underwater. To address this challenge, we propose in this letter a modern and scalable framework, NeuRSS, for SSS SLAM based on DR and loop closures (LCs) over large timescales, with an elevation prior provided by the bathymetric estimate using neural rendering from SSS. This framework is an iterative procedure that improves localization and bathymetric mapping. Initially, the bathymetry estimated from SSS using the DR estimate, though crude, can provide an important elevation prior in the nonlinear least-squares (NLS) optimization that estimates the relative pose between two loop-closure vertices in a pose graph. Subsequently, the global pose estimate from the SLAM component improves the positioning estimate of the vehicle, thus improving the bathymetry estimation. We validate our localization and mapping approach on two large surveys collected with a surface vessel and an AUV, respectively. We evaluate their localization results against the ground truth and compare the bathymetry estimation against data collected with multibeam echo sounders (MBES). Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous underwater vehicles

Subgradient Langevin Methods for Sampling from Non-smooth Potentials

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arXiv 2023年

作者： Habring, Andreas Holler, Martin Pock, Thomas Department of Mathematics and Scientific Computing University of Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Austria

This paper is concerned with sampling from probability distributions π on Rd admitting a density of the form π(x) ∝ e−U(x), where U(x) = F(x) + G(Kx) with K being a linear operator and G being non-differentiable. Two different methods are proposed, both employing a subgradient step with respect to G ◦ K, but, depending on the regularity of F, either an explicit or an implicit gradient step with respect to F. For both methods, non-asymptotic convergence proofs are provided, with improved convergence results for more regular F. Further, numerical experiments are conducted for simple 2D examples, illustrating the convergence rates, and for examples of Bayesian imaging, showing the practical feasibility of the proposed methods for high dimensional *** Codes 65C40, 65C05, 68U10, 65C60 Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse problems