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Efficient Recovery of Multi-Camera Motion from Two Affine Correspondences

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Efficient Recovery of Multi-Camera Motion from Two Affine Co...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Banglei Guan Ji Zhao Daniel Barath Friedrich Fraundorfer College of Aerospace Science and Engineering National University of Defense Technology China Centre for Machine Perception Czech Technical University Czech Republic Machine Perception Research Laboratory MTA SZTAKI Hungary Institute for Computer Graphics and Vision Graz University of Technology Austria Remote Sensing Technology Institute German Aerospace Center Germany

We propose an efficient method to estimate the relative pose of a multi-camera system from a minimum of two affine correspondences (ACs). Our solution is novel as it computes the 6DOF relative pose by utilizing a first-order rotation approximation. We directly derive a single polynomial based on the constraint between ACs and the generalized camera model. Then a closed-form solution is found analytically and it produces an accurate relative pose estimation efficiently. Benefiting from the low number of exploited correspondences and the speed of the solver, it speeds up robust estimators, e.g. RANSAC, significantly. The proposed method is evaluated both on synthetic data and real-world image sequences from the KITTI benchmark. It is shown that the proposed solver is superior to the state-of-the-art algorithms in terms of accuracy.

关键词： Closed-form solutions Automation Computational modeling Roads Conferences Pose estimation Benchmark testing

Interplay of Machine Learning and Software Engineering for Quality Estimations

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Interplay of Machine Learning and Software Engineering for Q...

2020 IEEE International Conference on Communications, Computing, Cybersecurity, and Informatics, CCCI 2020

作者： Abubakar, Hamza Obaidat, M.S. Gupta, Aaryan Bhattacharya, Pronaya Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad Gujarat India College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates King Abdullah II School of Information Technology University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing10008 China

ISBN: (纸本)9781728120355

In this era, the agile mindset has innovated the traditional software engineering (SE) process through the integration of DevOps flow engines, scrum iterations, and automation of continuous integration (CI) and continuous deployment (CD) cycles. However, the CI/CD integration requires manual code-revisions and refactoring at large scales. Recently, machine-learning (ML) is employed in SE that allows legacy codes to be highly dynamic, less coupling in related modules, automatic code versioning, and refactoring, with less coupling among related modules. However, over time, ML models tend to become bulky, with increasing monotonic losses during model training. To address this, SE techniques like code revisions are employed over ML codes to allow low-order training losses, that enables seamless and precise workflow structures. Motivated from the aforementioned discussions, the paper presents a systematic review of the close interplay of SE and ML and possible interactions in different applications. Suitable research questions and case studies are presented for possible adoption scenarios that depict the close ML-SE interplay share with each other, with the concluding remarks. The paper forms useful insights for ML engineers, data science practitioners, and SE quality estimators towards the building of efficient and scalable software solutions. © 2020 IEEE.

关键词： Machine learning

Towards computer-Using Personal Agents

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arXiv 2025年

作者： Bonatti, Piero A. Domingue, John Gentile, Anna Lisa Harth, Andreas Hartig, Olaf Hogan, Aidan Hose, Katja Jimenez-Ruiz, Ernesto McGuinness, Deborah L. Sun, Chang Verborgh, Ruben Wright, Jesse University of Naples Federico II Naples Italy Knowledge Media Institute The Open University Milton Keynes United Kingdom IBM Research San JoseCA United States Friedrich-Alexander-Universität Erlangen-Nürnberg Fraunhofer Institute for Integrated Circuits IIS Nürnberg Germany Linköping University Linköping Sweden DCC Universidad de Chile IMFD Santiago Chile TU Wien Vienna Austria City St George’s University of London London United Kingdom Rensselaer Polytechnic Institute TroyNY United States Maastricht University Maastricht Netherlands IDLab ELIS Ghent University imec Ghent Belgium Department of Computer Science University of Oxford Oxford United Kingdom

computer-Using Agents (CUA) enable users to automate increasingly-complex tasks using graphical interfaces such as browsers. As many potential tasks require personal data, we propose computer-Using Personal Agents (CUPAs) that have access to an external repository of the user’s personal data. Compared with CUAs, CUPAs offer users better control of their personal data, the potential to automate more tasks involving personal data, better interoperability with external sources of data, and better capabilities to coordinate with other CUPAs in order to solve collaborative tasks involving the personal data of multiple users. © 2025, CC BY.

关键词： Interoperability

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

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arXiv 2021年

作者： Pepe, Antonio Egger, Jan Codari, Marina Willemink, Martin J. Gsaxner, Christina Li, Jianning Roth, Peter M. Mistelbauer, Gabriel Schmalstieg, Dieter Fleischmann, Dominik Graz University of Technology Institute of Computer Graphics and Vision Inffeldgasse 16/II Graz8010 Austria Stanford University School of Medicine 3D and Quantitative Imaging Lab 300 Pasteur Drive StanfordCA94305 United States Laboratory Graz Austria Girardetstraße 2 Essen45131 Germany Otto-von-Guericke University Department of Simulation and Graphics. Universitätsplatz 2 Magdeburg39106 Germany

Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the cross-sectional planes is defined manually by highly trained operators. We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task, despite the presence of a non-negligible interoperator variability in the measurements. Impact: Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations. Methodology: In this work, we combine convolutional neural networks and uncertainty quantification methods to predict the orientation of such cross-sectional planes. We use clinical data randomly processed by 11 operators for training, and test on a smaller set processed by 3 independent operators to assess interoperator variability. Results: Our analysis shows that manual selection of cross-sectional planes is characterized by 95% limits of agreement (LOA) of 10.6◦ and 21.4◦ per angle. Our method showed to decrease static error by 3.57◦ (40.2%) and 4.11◦ (32.8%) against state of the art and LOA by 5.4◦ (49.0%) and 16.0◦ (74.6%) against manual processing. Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections. Copyright © 2021, The Authors. All rights reserved.

关键词： Blood vessels

A Robust Subtree-Based Authentication Scheme for TMIS Using Chaotic Hash Function

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A Robust Subtree-Based Authentication Scheme for TMIS Using ...

International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Chandrashekhar Meshram Mohammad S. Obaidat Balqies Sadoun Agbotiname Lucky Imoize Vishesh P. Gaikwad Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post Graduate College College of Chhindwara University Betul M.P. India King Abdullah II School of Information Technology University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University SRM Nagar Kattankulathur TN India Amity University Noida UP India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat Gujarat India

ISBN: (数字)9798350359091

ISBN: (纸本)9798350359107

The widespread availability of broadband telecommunication and the relatively affordable cost of vital signs monitoring devices have enabled telemedicine to bring its benefits directly to patients' homes. In contemporary healthcare, Telecare Medicine Information System (TMIS) facilitates remote disease diagnosis, treatment, and healthcare delivery through the capabilities of wireless communication and information technology. To protect patient confidentiality, TMIS must develop robust authentication systems since it operates on the public Internet, which exposes its operations to security vulnerabilities. To ensure patient privacy, we present a novel Subtree-based Authentication technique for TMIS. An evaluation of the proposed scheme reveals that it is resistant to known potential attacks, and offers more security effectiveness and resource efficiency than existing schemes, making it an excellent solution for enhancing the security of TMIS implementations.

关键词： Privacy Chaotic communication Telemedicine Authentication Passwords Internet Security

Schrödinger's ballot: Quantum Information and the violation of Arrow's impossibility theorem

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arXiv 2021年

作者： Sun, Xin He, Feifei Kulicki, Piotr Sopek, Mirek Department of the Foundations of Computer Science John Paul II Catholic University of Lublin Lublin Poland Quantum Blockchains Inc. Pulawy Poland Institute of Logic and Cognition Sun Yat-sen University Guangzhou China MakoLab SA Lodz Poland

In this paper we study Arrow's Impossibility Theorem in the quantum setting. Our work is based on the work of Bao and Halpern [3], in which it is proved that the quantum analogue of Arrow's Impossibility Theorem is not valid. However, we feel unsatisfied about the proof presented in [3]. Moreover, the definition of Quantum Independence of Irrelevant Alternatives (QiiA) in [3] seems not appropriate to us. In this paper, we give a better definition of QiiA, which properly captures the idea of the independence of irrelevant alternatives, and a detailed proof of the violation of Arrow's Impossibility Theorem in the quantum setting with the modified definition. © 2021, CC BY.

关键词： Quantum optics

Engaging with emotions - The role of emotion in HCI 20

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Engaging with emotions - The role of emotion in HCI

20th British computer Society Conference on Human-computer Interaction: Engage, HCI 2006

作者： Peter, Christian Crane, Elizabeth Axelrod, Lesley Beale, Russell Fraunhofer Institute for Computer Graphics Joachim Jungius Str. 11 Rostock18059 Germany University of Michigan 401 Washtenaw Ave Ann ArborMI48108 United States School of Information Systems Computing and Mathematics Brunel University Uxbridge MiddlesexUB8 3PH United Kingdom Advanced Interaction Group School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Emotions are of increasing interest to the HCI community and there is an enthusiastic spirit of adventure amongst researchers exploring this area. Much technical research has been done focussing on development of various proof-of-possibility studies and prototypical emotion components, but work done to date has been fragmented and lacks coherence. The goal of this workshop is to share information among researchers and practitioners working in this field through organized discussion and thematic working groups. This workshop will continue bringing people together, forming a community, supporting networking and collaboration to advocate coordinated work in this fascinating field of research. © BCS HCI Group Conference: Engage, HCI *** right reserved.

关键词： Human computer interaction

An Effective Learning Management System for Revealing Student Performance Attributes

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arXiv 2024年

作者： Zhang, Xinyu Lee, Vincent C.S. Xu, Duo Chen, Jun Obaidat, Mohammad S. The School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China The Department of Data Science and Artificial Intelligence Faculty of IT Monash University Melbourne3800 Australia Guangyuan Foreign Language School Guangyuan628018 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India

Contribution: A learning management system (LMS) incorporated with an advanced educational data mining module is proposed, as a means to mine efficiently from student performance records to provide valuable insights for educators in helping plan effective learning pedagogies, improve curriculum design, and guarantee quality of teaching. Background: An LMS streamlines the management of the teaching process in a centralized place, recording, tracking, and reporting the delivery of educational courses and student performance. Educational knowledge discovery from such an elearning system plays a crucial role in rule regulation, policy establishment, and system development. However, existing LMSs do not have embedded mining modules to directly extract knowledge. As educational modes become more complex, educational data mining efficiency from those heterogeneous student learning behaviours is gradually degraded. Intended outcomes: The design and application of the LMS enable educators to learn from past student performance experiences, empowering them to guide and intervene with students in time, and eventually improve their academic success. Application design: This study proposes an effective LMS which processes the stored data through an advanced educational data mining module. The mining module utilizes probability-based frequent pattern mining for generating candidate itemsets and updating LMS databases in each iteration for complexity reduction. In addition, the conditional probability increment ratio (CPIR) is deployed as an exceptionality measure to mine effective common and exception rules simultaneously. The iteratively generated rules can reveal academic performance patterns so that have merits in learning personal goal achievement. Findings: Through two illustrative case studies, experimental results demonstrate increased mining efficiency of the proposed mining module without information loss compared to classic educational mining algorithms. The mined knowl

关键词： Data mining

Health@Hand A Visual Interface for eHealth Monitoring

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Health@Hand A Visual Interface for eHealth Monitoring

IEEE Symposium on computers and Communications (ISCC)

作者： Lars Nonnemann Marian Haescher Mario Aehnelt Gerald Bieber Holger Diener Bodo Urban Institute of Computer Science University of Rostock Germany Fraunhofer Institute for Computer Graphics Research Rostock Germany

The digitization within the healthcare sector is an extensive topic for multiple ICT technologies, such as Cloud Computing, Internet of Things (IoT), and Artificial Intelligence. Over the past years, there has been an increasing interest in the storage, evaluation and visual representation of medical data. This led to a substantial amount of independent eHealth technologies, that are primarily concerned with information gathering and management. However, the multitude of existing tools makes it is difficult for the user to understand global relationships between different management systems. Therefore, we present Health@Hand an IoT system for monitoring vital and administrative data in the digital twin of an intensive care unit (ICU). The main goal of our system is to summarize and process real-time data in order to assist medical experts such as physician, head-nurses or controllers in their daily tasks. Health@Hand achieves this by providing a visual interface for the management and analysis of different medical data sources.

关键词： Monitoring Medical services Tools Biomedical monitoring Data visualization Data models Visualization