A database for publications published by researchers and students at SimulaMet.
Research area
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- All (370)
- Journal articles (136)
- Books (2)
- Edited books (1)
- Proceedings, refereed (165) Remove Proceedings, refereed <span class="counter">(165)</span> filter
- Book chapters (6)
- Talks, keynote (1)
- PhD theses (5)
- Proceedings, non-refereed (2)
- Posters (8)
- Talks, invited (18)
- Talks, contributed (15)
- Public outreach (3)
- Miscellaneous (8)
Proceedings, refereed
A Comparative Study of Interactive Environments for Investigative Interview of A Virtual Child Avatar
In IEEE international symposium on multimedia (ISM). IEEE, 2022.Status: Published
A Comparative Study of Interactive Environments for Investigative Interview of A Virtual Child Avatar
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | IEEE international symposium on multimedia (ISM) |
Pagination | 194-201 |
Publisher | IEEE |
DOI | 10.1109/ISM55400.2022.00043 |
Proceedings, refereed
A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents
In Proceedings of SAI Intelligent Systems Conference. Springer, 2021.Status: Published
A Deep Learning-Based Tool for Automatic Brain Extraction from Functional Magnetic Resonance Images of Rodents
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proceedings of SAI Intelligent Systems Conference |
Pagination | 549–558 |
Publisher | Springer |
Automated Clipping of Soccer Events using Machine Learning
In IEEE International Symphosium of Multimedia (ISM). IEEE, 2021.Status: Published
Automated Clipping of Soccer Events using Machine Learning
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | IEEE International Symphosium of Multimedia (ISM) |
Date Published | 12/2021 |
Publisher | IEEE |
DOI | 10.1109/ISM52913.2021.00042 |
Proceedings, refereed
A Latency Compensation Technique Based on Game Characteristics to Mitigate the Influence of Delay on Cloud Gaming Quality of Experience
In ACM Multimedia Systems Conference 2020 (MMSys 2020). New York, NY, USA: ACM, 2020.Status: Published
A Latency Compensation Technique Based on Game Characteristics to Mitigate the Influence of Delay on Cloud Gaming Quality of Experience
Cloud Gaming (CG) is an immersive multimedia service that promises many benefits. In CG, the games are rendered in a cloud server, and the resulted scenes are streamed as a video sequence to the client. Using CG users are not forced to update their gaming hardware frequently, and available games can be played on any operating system or suitable device. However, cloud gaming requires a reliable and low-latency network, which makes it a very challenging service. Transmission latency strongly affects the playability of a cloud game and consequently reduces the users' Quality of Experience (QoE). In this paper, we propose a latency compensation technique using game adaptation that mitigates the influence of delay on QoE. This technique uses five game characteristics for the adaptation. These characteristics, in addition to an Aim-assistance technique, were implemented in four games for evaluation. A subjective study using 194 participants was conducted using a crowdsourcing approach. The results showed that the majority of the proposed adaptation techniques lead to significant improvements in the cloud gaming QoE.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | ACM Multimedia Systems Conference 2020 (MMSys 2020) |
Pagination | 15-25 |
Publisher | ACM |
Place Published | New York, NY, USA |
URL | https://dl.acm.org/doi/abs/10.1145/3339825.3391855 |
DOI | 10.1145/3339825.3391855 |
ACM Multimedia BioMedia 2020 Grand Challenge Overview
In Proceedings of the 28th ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery, 2020.Status: Published
ACM Multimedia BioMedia 2020 Grand Challenge Overview
The BioMedia 2020 ACM Multimedia Grand Challenge is the second in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, participants are asked to develop algorithms that automatically predict the quality of a given human semen sample using a combination of visual, patient-related, and laboratory-analysis-related data. Compared to last year's challenge, participants are provided with a fully multimodal dataset (videos, analysis data, study participant data) from the field of assisted human reproduction. The tasks encourage the use of the different modalities contained within the dataset and finding smart ways of how they may be combined to further improve prediction accuracy. For example, using only video data or combining video data and patient-related data. The ground truth was developed through a preliminary analysis done by medical experts following the World Health Organization's standard for semen quality assessment. The task lays the basis for automatic, real-time support systems for artificial reproduction. We hope that this challenge motivates multimedia researchers to explore more medical-related applications and use their vast knowledge to make a real impact on people's lives.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | Proceedings of the 28th ACM International Conference on Multimedia |
Pagination | 4655–4658 |
Publisher | Association for Computing Machinery |
Place Published | New York, NY, USA |
ISBN Number | 9781450379885 |
Keywords | artificial intelligence, Machine learning, male fertility, semen analysis, spermatozoa |
URL | https://doi.org/10.1145/3394171.3416287 |
DOI | 10.1145/3394171.3416287 |
An Optimization Framework for RegularizedLinearly Coupled Matrix-Tensor Factorization
In European Signal Processing Conference (EUSIPCO). IEEE, 2020.Status: Published
An Optimization Framework for RegularizedLinearly Coupled Matrix-Tensor Factorization
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | European Signal Processing Conference (EUSIPCO) |
Pagination | 985-989 |
Publisher | IEEE |
URL | https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0000985.pdf |
Assessing Interactive Gaming Quality of Experience Using a Crowdsourcing Approach
In 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2020.Status: Published
Assessing Interactive Gaming Quality of Experience Using a Crowdsourcing Approach
Traditionally, the Quality of Experience (QoE) is assessed in a controlled laboratory environment where participants give their opinion about the perceived quality of a stimulus on a standardized rating scale. Recently, the usage of crowdsourcing micro-task platforms for assessing the media quality is increasing. The crowdsourcing platforms provide access to a pool of geographically distributed, and demographically diverse group of workers who participate in the experiment in their own working environment and using their own hardware. The main challenge in crowdsourcing QoE tests is to control the effect of interfering influencing factors such as a user's environment and device on the subjective ratings. While in the past, the crowdsourcing approach was frequently used for speech and video quality assessment, research on a quality assessment for gaming services is rare. In this paper, we present a method to measure gaming QoE under typically considered system influence factors including delay, packet loss, and framerates as well as different game designs. The factors are artificially manipulated due to controlled changes in the implementation of games. The results of a total of five studies using a developed evaluation method based on a combination of the ITU-T Rec. P.809 on subjective evaluation methods for gaming quality and the ITU-T Rec. P.808 on subjective evaluation of speech quality with a crowdsourcing approach will be discussed. To evaluate the reliability and validity of results collected using this method, we finally compare subjective ratings regarding the effect of network delay on gaming QoE gathered from interactive crowdsourcing tests with those from equivalent laboratory experiments.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) |
Publisher | IEEE |
URL | https://ieeexplore.ieee.org/abstract/document/9123122 |
DOI | 10.1109/QoMEX48832.2020.9123122 |
Proceedings, refereed
A Web-Based Software for Training and Quality Assessment in the Image Analysis Workflow for Cardiac T1 Mapping MRI
In 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019.Status: Published
A Web-Based Software for Training and Quality Assessment in the Image Analysis Workflow for Cardiac T1 Mapping MRI
Medical practice makes significant use of imaging scans such as Ultrasound or MRI as a diagnostic tool. They are used in the visual inspection or quantification of medical parameters computed from the images in post-processing. However, the value of such parameters depends much on the user's variability, device, and algorithmic differences. In this paper, we focus on quantifying the variability due to the human factor, which can be primarily addressed by the structured training of a human operator. We focus on a specific emerging cardiovascular \gls{mri} methodology, the T1 mapping, that has proven useful to identify a range of pathological alterations of the myocardial tissue structure. Training, especially in emerging techniques, is typically not standardized, varying dramatically across medical centers and research teams. Additionally, training assessment is mostly based on qualitative approaches. Our work aims to provide a software tool combining traditional clinical metrics and convolutional neural networks to aid the training process by gathering contours from multiple trainees, quantifying discrepancy from local gold standard or standardized guidelines, classifying trainees output based on critical parameters that affect contours variability.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | 2019 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
DOI | 10.1109/ISM46123.2019.00047 |
ACM Multimedia BioMedia 2019 Grand Challenge Overview
In The ACM International Conference on Multimedia (ACM MM). New York, New York, USA: ACM Press, 2019.Status: Published
ACM Multimedia BioMedia 2019 Grand Challenge Overview
The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year’s challenge, the participants are asked to develop efficient algorithms which automatically detect a variety of findings commonly identified in the gastrointestinal (GI) tract (a part of the human digestive system). The purpose of this task is to develop methods to aid medical doctors performing routine endoscopy inspections of the GI tract. In this paper, we give a detailed description of the four different tasks of this year’s challenge, present the datasets used for training and testing, and discuss how each submission is evaluated both qualitatively and quantitatively.
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | The ACM International Conference on Multimedia (ACM MM) |
Pagination | 2563-2567 |
Date Published | 10/2019 |
Publisher | ACM Press |
Place Published | New York, New York, USA |
ISBN Number | 9781450368896 |
URL | http://dl.acm.org/citation.cfm?doid=3343031http://dl.acm.org/citation.cf... |
DOI | 10.1145/334303110.1145/3343031.3356058 |
Proceedings, refereed
ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers
In EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference. IEEE, 2017.Status: Published
ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers
Afilliation | Machine Learning |
Project(s) | Department of Data Science and Knowledge Discovery |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference |
Publisher | IEEE |
DOI | 10.23919/EUSIPCO.2017.8081286 |