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HPI Research School on Service-Oriented Systems engineering 2020 Fall Retreat

作者： Fricke, Andreas Computer Graphics Systems Group Hasso Plattner Institute for Digital Engineering Germany

This report describes my recent activities and research work on Service-oriented Systems engineering in the HPI Research School as a continuation of my Fall Report 2019 and my Spring Report 2020. © 2021 Universit... 详细信息

ISBN: (纸本)9783869565132

关键词： engineering research

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engineering Technology Capstone: Exploring the Current State

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Journal of engineering Technology 2024年第2期41卷 34-40页

作者： Webster, Rustin Turner, Matthew Purdue Polytechnic Institute School of Engineering Technology Purdue University mechanical engineering and computer graphics technology United States Purdue University’s School of Engineering Technology United States

The characteristics that make up the general identity of engineering technology (ET) degree programs and their graduates are well known;however, the explicit characteristics of ET capstone nationally is unknown. In other words, there are multiple gaps in the general ET capstone body of knowledge that need to be filled in order to fully understand and describe the current state. The purpose of this short paper is to provide a synopsis of 1) the current known identity of ET education, 2) existing ET capstone research, and 3) ET capstone knowledge gaps (i.e., future research areas). Internal preliminary findings comparing traditional engineering degree programs and ET capstone experiences include differences in duration, project types and sources, budgets, assessments, etc. Additionally, findings show that ET degree programs were not included in multiple large-scale national capstone-based research studies investigating current and longitudinal practices, design teaching taxonomies, and graduate career readiness. Deeper insights into ET capstone practices are needed and can help empower stakeholders (e.g., faculty, administrators, researchers, etc.) to articulate ET’s value more effectively within the broader landscape of science, technology, engineering, and mathematics (STEM), and hopefully spark interest in new research collaborations within the general capstone community in order to address identified knowledge gaps. A better understood and possibly improved ET capstone experience could help better prepare graduates to transition into the workforce by minimizing the time needed to acquire new knowledge, skills, and abilities after being hired. © 2024, American Society for engineering Education. All rights reserved.

关键词： Budget control

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Point Cloud Analytics: Proposed Future Work

Point Cloud Analytics: Proposed Future Work

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HPI Research School on Service-Oriented Systems engineering 2020 Fall Retreat

作者： Stojanovic, Vladeta Computer Graphics Systems Group Hasso Plattner Institute for Digital Engineering Germany

ISBN: (纸本)9783869565132

Automated processing, semantic-enrichment and visual analytics methods for point clouds are often use-case specific for a given domain (e.g. for Facility Management (FM) applications). Currently, this means that applicable processing techniques, semantics and visual analytics methods need to be selected, generated or implemented by human domain experts, which is a rather error-prone, subjective and non-interoperable process. An ontology-driven analytics approach could be used to solve this problem by creating and maintaining a dynamic knowledge structure and utilizing an ontology for inferencing optimal selection of automated processing and analytics techniques of point cloud data. © 2021 Universitatsverlag Potsdam. All rights reserved.

关键词： Ontology

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Intrinsic Editing of RGB-D Images on Smartphones

Intrinsic Editing of RGB-D Images on Smartphones

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HPI Research School on Service-Oriented Systems engineering 2020 Fall Retreat

作者： Shekhar, Sumit Computer Graphics Systems Group Hasso Plattner Institute for Digital Engineering Germany

This report describes my recent activities on Service-oriented Systems engineering between April 2020 and October 2020 with the HPI Research School, and summarizes my research and teaching activities. © 2021 Univ...

ISBN: (纸本)9783869565132

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Image-Based Classification of Transport Infrastructure in Mobile Mapping 3D Point Clouds

Image-Based Classification of Transport Infrastructure in Mo...

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HPI Research School on Service-Oriented Systems engineering 2020 Fall Retreat

作者： Wolf, Johannes Computer Graphics Systems Group Hasso Plattner Institute for Digital Engineering Germany

ISBN: (纸本)9783869565132

Capturing urban areas and transport infrastructure for automated analysis processes becomes ever more important. Laserscanning and photogrammetry are used for scanning the environment in highly detailed resolution. This report presents techniques for the semantic classification of 3D point clouds from mobile mapping scans based on rendering and analyzing images. Objects of interest are primarily road markings and railroad tracks. The approach renders 3D point cloud input data into images for which U-Net as an established image recognition convolutional neural network is used for the semantic classification. The results of the classification are projected back into the 3D point cloud. An automated extraction of vector data can be applied for detected objects to generate detailed maps. The automatically generated shape files created by the presented process can be further used in various GIS applications. The results of implemented out-of-core techniques show that the approach can efficiently be applied on large datasets. © 2021 Universitatsverlag Potsdam. All rights reserved.

关键词： Image recognition

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Microsnoop: A generalist tool for microscopy image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun Rui Wang Xingcai Zhang Yi Wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of Computer-Aided Design&Computer Graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University Hangzhou 310018China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised *** can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment *** benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom *** can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal ***,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing *** will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware image representation

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SelfMAD: Enhancing Generalization and Robustness in Morphing Attack Detection via Self-Supervised Learning

arXiv

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arXiv 2025年

作者： Ivanovska, Marija Todorov, Leon Damer, Naser Jain, Deepak Kumar Peer, Peter Štruc, Vitomir Faculty of Electrical Engineering University in Ljubljana Slovenia Faculty of Computer and Information Science University in Ljubljana Slovenia Dalian University of Technology China Fraunhofer Institute for Computer Graphics Research Germany

With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious activities. Contemporary Morphing Attack Detection (MAD) approaches frequently rely on supervised, discriminative models trained on examples of bona fide and morphed images. These models typically perform well with morphs generated with techniques seen during training, but often lead to sub-optimal performance when subjected to novel unseen morphing techniques. While unsupervised models have been shown to perform better in terms of generalizability, they typically result in higher error rates, as they struggle to effectively capture features of subtle artifacts. To address these shortcomings, we present SelfMAD, a novel self-supervised approach that simulates general morphing attack artifacts, allowing classifiers to learn generic and robust decision boundaries without overfitting to the specific artifacts induced by particular face morphing methods. Through extensive experiments on widely used datasets, we demonstrate that SelfMAD significantly outperforms current state-of-the-art MADs, reducing the detection error by more than 64% in terms of EER when compared to the strongest unsupervised competitor, and by more than 66%, when compared to the best performing discriminative MAD model, tested in cross-morph settings. The source code for SelfMAD is available at https://***/LeonTodorov/SelfMAD. © 2025, CC BY-NC-ND.

关键词： Self-supervised learning

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Towards Neuroadaptive Augmented Reality Piano Tutorials

Towards Neuroadaptive Augmented Reality Piano Tutorials

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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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FX-MAD: Frequency-Domain Explainability and Explainability-Driven Unsupervised Detection of Face Morphing Attacks

FX-MAD: Frequency-Domain Explainability and Explainability-D...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Marco Huber Pedro C. Neto Ana F. Sequeira Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Germany INESC TEC Porto Portugal Faculry of Engineering University of Porto Portugal

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner.

关键词： computer vision Correlation Face recognition Soft sensors Frequency-domain analysis Conferences Benchmark testing Security Facial features

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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