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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Wolfgang A. Mehringer Markus Gerhard Wirth Stefan Gradl Luis S. Durner Matthias Ring Annemarie F Laudanski Bjoern Eskofier Georg Michelson Machine Learning and Data Analytics Lab University of Erlangen-Nürnberg (FAU) University of Waterloo Talkingeyes&More GmbH University of Erlangen-Nürnberg (FAU)

ISBN: (数字)9781728176758

ISBN: (纸本)9781728176765

Strabismus is a visual disorder characterized by eye misalignment. The effect of Panum's Fusional Area (PFA) compensates for small misalignments. However, prominent misalignments affect binocular vision and when present in childhood it may lead to amblyopia, a developmental disorder of the visual system. With the advent of Virtual Reality (VR) technology, possibilities for novel binocular treatments to amblyopia arise in which the measurement of strabismus is crucial to correctly compensate for it. Thus, VR yields great potential due to the ability of displaying content to each eye independently. Major research in VR addresses this topic using eye-tracking while there is a paucity of research on image-based assessment methods. In this work, we propose a VR application for measuring strabismus in nine lines of sight. We conducted a study with 14 healthy participants to evaluate the system under two conditions: no strabismus and an artificial deviation induced by prism lenses. Further, we evaluated the effect of PFA on the system by measuring its extent in horizontal and vertical lines of sight. Results show significant difference between the expected deviation induced by prism lenses and the measured deviation. The existing difference within the measurements can be explained with the recorded extent of the PFA.

关键词： Visualization Atmospheric measurements Particle measurements Vision defects Augmented reality Lenses

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ViPR: Visual-odometry-aided pose regression for 6DoF camera localization

arXiv

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arXiv 2019年

作者： Ott, Felix Feigl, Tobias Löffler, Christoffer Mutschler, Christopher Nürnberg Germany Programming Systems Group FAU Erlangen-Nürnberg Germany Machine Learning and Data Analytics Lab FAU Erlangen-Nürnberg Germany

Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly from (a series of) images or by merging depth maps with the optical flow (OF), research that combines absolute pose regression with OF is limited. We propose ViPR, a novel architecture for long-term 6DoF VO that leverages synergies between absolute pose estimates (from PoseNet-like architectures) and relative pose estimates (from FlowNet-based architectures) by combining both through recurrent layers. Experiments with known publicly availab.e datasets and with our own Industry dataset show that our novel design outperforms existing techniques in long-term navigation tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer vision

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Convolutional Neural Networks for Position Estimation in TDoA-Based Locating Systems

Convolutional Neural Networks for Position Estimation in TDo...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Arne Niitsoo Thorsten Edelhäußer Christopher Mutschler Machine Learning and Information Fusion Group Fraunhofer Institute for Integrated Circuits IIS Nuremberg Germany Maching Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

Object localization and tracking is essential for many applications including logistics and industry. Many local Time-of-Flight (ToF)-based locating systems use synchronized antennas to receive radio signals emitted by mobile tags. They detect the Time-of-Arrival (TOA) of the signal at each antenna and trilaterate the position from the Time Difference-of-Arrival (TDoA) between antennas. However, in multipath scenarios it is difficult to extract the correct ToA. This causes wrong positions. This paper proposes a signal processing method that uses deep learning to estimate the absolute tag position directly from the raw channel impulse response (CIR) data. We use the CIR together with ground truth positional data to train a convolutional neural network (CNN) that not only estimates non-linearities in the signal propagation space but also analyzes the signal for multipath effects. Our evaluation shows that our position estimation works in multipath environments and also outperforms classical signal processing in line-of-sight situations.

关键词： Feature extraction Neurons Antennas Estimation Convolution Convolutional neural networks

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization 40

BIM: Improving Graph Neural Networks with Balanced Influence...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Gao, Xinyi Yang, Ling Cao, Meng Huang, Ping Shan, Jiulong Yin, Hongzhi Cui, Bin Peking University Center for Machine Learning Research China Institute of Advanced Algorithms Research Shanghai China National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Peking University Key Lab of High Confidence Software Technologies China Apple Inc. Institute of Computational Social Science Peking University Qingdao China

ISBN: (纸本)9798350317152

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of lab.led nodes, i.e., lab.led nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo lab.l to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score. © 2024 IEEE.

关键词： Graph neural networks

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OculusGraphy: Description and Time Domain Analysis of Full-Field Electroretinograms database

OculusGraphy: Description and Time Domain Analysis of Full-F...

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Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

作者： Faisal B. Albasu Subhankar Dey Anton Yu. Dolganov Oussama EL Hamzaoui Wisam M. Mustafa Aleksei E. Zhdanov Engineering School of Information Technologies Telecommunications and Control Systems Ural Federal University Yekaterinburg Russia Machine Learning and Data Analytics Lab University of Erlangen-Nuremberg Erlangen Germany

Electroretinography is an established electrophysiological technique for clinical investigation of visual health function. The test involves the non-invasive assessment of the retinal function using light stimulation. This study presents a comprehensive database of five types of signals, including Scotopic 2.0 ERG response, Photopic 2.0 ERG response, Maximum 2.0 ERG response, Photopic 2.0 EGR Flicker response and Scotopic 2.0 ERG Oscillatory Potentials. The database includes 1975 signals from 323 patients, consisting of both adults and children. This study seeks to evaluate and analyze the classical parameters of three of the five signals, specifically Scotopic 2.0 ERG response, Photopic 2.0 ERG response, and Maximum 2.0 ERG response, using time-domain analysis. Correlation analysis was conducted in order to gain insights into the potential relationship between a patient's age and the classical parameters. However, the results suggest little to no correlation between these factors. The database presented in this study will serve as a useful resource for further research in the field of electrophysiology. Future research in this area could explore the use of other advanced analysis methods, such as frequency and time-frequency domain analyses, in order to gain further insights into the nuances of these signals and to better differentiate between healthy patients and those with pathologies. Additionally, further research is needed to establish norms for pediatric signals in order to better understand retinal health in children.

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NC-ALG: Graph-Based Active learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated lab.ls are always correct, which is contradictory to the error-prone lab.ling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the lab.ling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node lab.ling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node lab.ling, NC-ALG significantly reduces the lab.ling cost by considering the model-predicted lab.ls and the lab.ls of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms lab.ling efficiency. Notably, it only takes NC-ALG one-third of the lab.ling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Smart Annotation Tool for Multi-sensor Gait-based Daily Activity data

Smart Annotation Tool for Multi-sensor Gait-based Daily Acti...

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IEEE International Conference on Pervasive Computing and Communications

作者： Christine F. Martindale Nils Roth Julius Hannink Sebastijan Sprager Bjoern M. Eskofier Machine Learning and Data Analytics Lab Computer Science Department Friedrich-Alexander University Erlangen-Nürnberg Germany Faculty of Computer and Information Science University of Ljubljana Slovenia

ISBN: (纸本)9781538632284

The monitoring of patients within a natural, home environment is important in order to close knowledge gaps in the treatment and care of neurodegenerative diseases, such as quantifying the daily fluctuation of Parkinson's patients' symptoms. The combination of machine learning algorithms and wearable sensors for gait analysis is becoming capable of achieving this. However, these algorithms require large, lab.lled, realistic datasets for training. Most systems used as a ground truth for lab.lling are restricted to the lab.ratory environment, as well as being large and expensive. We propose a study design for a realistic activity monitoring dataset, collected with inertial measurement units, pressure insoles and cameras. It is not restricted by a fixed location or capture volume and still enables the lab.lling of gait phases or, where non-gait movement such as jumping occur: on-the-ground, off-the-ground phases. Additionally, this paper proposes a smart annotation tool which reduces annotation cost by more than 80%. This smart annotation is based on edge detection within the pressure sensor signal. The tool also enables annotators to perform assisted correction of these lab.ls in a post-processing step. This system enables the collection and lab.lling of large, fairly realistic datasets where 93% of the automatically generated lab.ls are correct and only an additional 10% need to be inserted manually. Our tool and protocol, as a whole, will be useful for efficiently collecting the large datasets needed for training and validation of algorithms capable of cyclic human motion analysis in natural environments.

关键词： Image edge detection Tools Cameras lab.ling Pressure sensors Intelligent sensors Edge detection Pressure Sensor Device Component Cameras intelligent sensors ANNOTATIONS lab.ratory environment dataset Wearable sensors Measurement units Research Design INSOLE Packaging

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Achieving Efficient and Realistic Full-Radar Simulations and Automatic data Annotation by Exploiting Ray Meta data from a Radar Ray Tracing Simulator

Achieving Efficient and Realistic Full-Radar Simulations and...

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IEEE National Conference on Radar

作者： Christian Schüβler Marcel Hoffmann Vanessa Wirth Björn Eskofier Tim Weyrich Marc Stamminger Martin Vossiek Institute of Microwaves and Photonics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Chair of Visual Computing Machine Learning and Data Analytics Lab Chair of Digital Reality

In this work, a novel radar simulation concept for efficiently simulating realistic radar data for range, Doppler, and arbitrary antenna positions is introduced. With the concept, the simulated radar signal can also be automatically annotated by splitting it into multiple parts. Annotations that are almost perfect - including the annotation of exotic effects, such as multi-path - can be produced with this approach. Signal parts originating from different parts of an object can be lab.lled with it as well. To this end, the computation process used in a Monte Carlo shooting and bouncing rays (SBR) simulator is adapted. By considering the hits of each simulated ray, various meta data can be stored, such as hit position, mesh pointer, object IDs, and many more. This collected meta data can then be utilized to predict path-length changes caused by object motion to obtain Doppler information or to apply specific ray filter rules to obtain radar signals that only fulfil specific conditions, such as multiple bounces, or signals that contain specific object IDs. Using this approach, perfect, and otherwise almost impossible, annotation schemes can be realized.

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Artificial Intelligence for Imaging Diagnostics in Neurosurgery

Artificial Intelligence for Imaging Diagnostics in Neurosurg...

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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Andrey Yu. Letyagin Sergey K. Golushko Jamil A. Rzaev Mihail E. Amelin Evgeniy N. Pavlovsky Bair N. Tuchinov Evgeniya V. Amelina Galina I. Moisak Ekaterina G. Bulgakova Liana O. Degtyareva Novosibirsk State University Novosibirsk Russia Federal State Budget Institution Federal Center of Neurosurgery Novosibirsk Russia FSBI “Federal Neurosurgical Center” Novosibirsk Russia Stread Data Analytics & Machine Learning lab. Novosibirsk State University Novosibirsk Russia

Improving the accuracy and timeliness of medical imaging in everyday clinical neurosurgery practice is an urgent problem for the health care of all countries. This problem will be solved with the help of artificial intelligence (AI). By performing the search and contouring of “suspicious” pathological objects, making automatic measurements on 3D medical images (MRI, CT), formalizing quantitative indicators and compiling medical statistical reports, AI can solve diagnostic problems in radiology and medical imaging. The effect of introducing AI will be at least a more accurate and quick interpretation of images, implemented through a set of decision-making tools for medical specialists.

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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