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Publications (10 of 13) Show all publications
Wellington, S., Wilson, H., Liwicki, F. S., Gupta, V., Saini, R., De, K., . . . Metcalfe, B. (2024). Improving inner speech decoding by hybridisation of bimodal EEG and fMRI data. In: 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): . Paper presented at 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 15-19, 2024, Orlando, USA. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Improving inner speech decoding by hybridisation of bimodal EEG and fMRI data
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2024 (English)In: 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Institute of Electrical and Electronics Engineers Inc. , 2024Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Series
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, ISSN 2375-7477, E-ISSN 2694-0604
National Category
Signal Processing
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-111442 (URN)10.1109/EMBC53108.2024.10781692 (DOI)2-s2.0-85214993740 (Scopus ID)
Conference
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), July 15-19, 2024, Orlando, USA
Note

ISBN for host publication: 979-8-3503-7149-9;

Funder: United Kingdom Research Institute (UKRI, grant EP/S023437/1); Engineering and Physical Sciences Research Council (EPSRC, grant EP/S515279/1); Grants for Excellent Research Projects Proposals of SRT.ai 2022;

Available from: 2025-01-28 Created: 2025-01-28 Last updated: 2025-02-05Bibliographically approved
Simistira Liwicki, F., Gupta, V., Saini, R., De, K., Abid, N., Rakesh, S., . . . Eriksson, J. (2023). Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition. Scientific Data, 10, Article ID 378.
Open this publication in new window or tab >>Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
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2023 (English)In: Scientific Data, E-ISSN 2052-4463, Vol. 10, article id 378Article in journal (Refereed) Published
Abstract [en]

The recognition of inner speech, which could give a ‘voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.

Place, publisher, year, edition, pages
Springer Nature, 2023
National Category
Computer Sciences Computer graphics and computer vision
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-98322 (URN)10.1038/s41597-023-02286-w (DOI)001006100600001 ()37311807 (PubMedID)2-s2.0-85161923014 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-06-13 (hanlid);

Funder: Grants for Excellent Research Projects Proposals of SRT.ai 2022

Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2025-02-05Bibliographically approved
Chhipa, P. C., Rodahl Holmgren, J., De, K., Saini, R. & Liwicki, M. (2023). Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?. In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023): . Paper presented at IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023), Paris, France, October 2-6, 2023 (pp. 4469-4478). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?
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2023 (English)In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023), Institute of Electrical and Electronics Engineers Inc. , 2023, p. 4469-4478Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
National Category
Computer graphics and computer vision Computer Sciences
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-103984 (URN)10.1109/ICCVW60793.2023.00481 (DOI)001156680304060 ()2-s2.0-85182928560 (Scopus ID)
Conference
IEEE/CVF International Conference on Computer Vision Workshops (ICCVW 2023), Paris, France, October 2-6, 2023
Note

ISBN for host publication: 979-8-3503-0745-0;

Available from: 2024-01-29 Created: 2024-01-29 Last updated: 2025-02-07
Rakesh, S., Liwicki, F., Mokayed, H., Upadhyay, R., Chhipa, P. C., Gupta, V., . . . Saini, R. (2023). Emotions Classification Using EEG in Health Care. In: Tistarelli, Massimo; Dubey, Shiv Ram; Singh, Satish Kumar; Jiang, Xiaoyi (Ed.), Computer Vision and Machine Intelligence: Proceedings of CVMI 2022. Paper presented at International Conference on Computer Vision & Machine Intelligence (CVMI), Allahabad, Prayagraj, India, August 12-13, 2022 (pp. 37-49). Springer Nature
Open this publication in new window or tab >>Emotions Classification Using EEG in Health Care
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2023 (English)In: Computer Vision and Machine Intelligence: Proceedings of CVMI 2022 / [ed] Tistarelli, Massimo; Dubey, Shiv Ram; Singh, Satish Kumar; Jiang, Xiaoyi, Springer Nature, 2023, p. 37-49Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer Nature, 2023
Series
Lecture Notes in Networks and Systems (LNNS) ; 586
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-98587 (URN)10.1007/978-981-19-7867-8_4 (DOI)2-s2.0-85161601282 (Scopus ID)
Conference
International Conference on Computer Vision & Machine Intelligence (CVMI), Allahabad, Prayagraj, India, August 12-13, 2022
Note

ISBN för värdpublikation: 978-981-19-7866-1, 978-981-19-7867-8

Available from: 2023-06-19 Created: 2023-06-19 Last updated: 2025-02-05Bibliographically approved
Chhipa, P. C., Chopra, M., Mengi, G., Gupta, V., Upadhyay, R., Chippa, M. S., . . . Liwicki, M. (2023). Functional Knowledge Transfer with Self-supervised Representation Learning. In: 2023 IEEE International Conference on Image Processing: Proceedings: . Paper presented at 30th IEEE International Conference on Image Processing, ICIP 2023, October 8-11, 2023, Kuala Lumpur, Malaysia (pp. 3339-3343). IEEE
Open this publication in new window or tab >>Functional Knowledge Transfer with Self-supervised Representation Learning
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2023 (English)In: 2023 IEEE International Conference on Image Processing: Proceedings, IEEE , 2023, p. 3339-3343Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2023
Series
Proceedings - International Conference on Image Processing, ISSN 1522-4880
National Category
Computer graphics and computer vision Computer Sciences
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-103659 (URN)10.1109/ICIP49359.2023.10222142 (DOI)001106821003077 ()2-s2.0-85180766253 (Scopus ID)978-1-7281-9835-4 (ISBN)978-1-7281-9836-1 (ISBN)
Conference
30th IEEE International Conference on Image Processing, ICIP 2023, October 8-11, 2023, Kuala Lumpur, Malaysia
Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2025-02-07Bibliographically approved
De, K. (2023). Investigating pretrained self-supervised vision transformers for reference-based quality assessment. In: : . Paper presented at IS and T International Symposium on Electronic Imaging: 20th Image Quality and System Performance, IQSP 2023, San Francisco, United States, January 16-19, 2023. Society for Imaging Science and Technology, 35, Article ID IQSP-308.
Open this publication in new window or tab >>Investigating pretrained self-supervised vision transformers for reference-based quality assessment
2023 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Society for Imaging Science and Technology, 2023
Series
IS&T International Symposium on Electronic Imaging Science and Technology, E-ISSN 2470-1173
Keywords
Full-reference Image Quality Assessment, Vision Transformers, Self-Supervised Learning (DINO)
National Category
Computer graphics and computer vision Computer Sciences
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-103562 (URN)10.2352/EI.2023.35.8.IQSP-308 (DOI)2-s2.0-85169569117 (Scopus ID)
Conference
IS and T International Symposium on Electronic Imaging: 20th Image Quality and System Performance, IQSP 2023, San Francisco, United States, January 16-19, 2023
Available from: 2024-01-12 Created: 2024-01-12 Last updated: 2025-02-01Bibliographically approved
Mokayed, H., Nayebiastaneh, A., De, K., Sozos, S., Hagner, O. & Backe, B. (2023). Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW): . Paper presented at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2023), Vancouver, Canada, June 18-22, 2023 (pp. 5314-5322). IEEE Computer Society
Open this publication in new window or tab >>Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions
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2023 (English)In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW), IEEE Computer Society, 2023, p. 5314-5322Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2023
Series
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, ISSN 2160-7508, E-ISSN 2160-7516
National Category
Computer graphics and computer vision
Research subject
Machine Learning; Centre - Centre for Distance-Spanning Technology (CDT)
Identifiers
urn:nbn:se:ltu:diva-103574 (URN)10.1109/CVPRW59228.2023.00560 (DOI)2-s2.0-85170825985 (Scopus ID)
Conference
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2023), Vancouver, Canada, June 18-22, 2023
Note

ISBN for host publication: 979-8-3503-0250-9(print); 979-8-3503-0249-3(electronic)

Available from: 2024-01-11 Created: 2024-01-11 Last updated: 2025-02-07Bibliographically approved
De, K. & Pedersen, M. (2022). Effect of hue shift towards robustness of convolutional neural networks. In: : . Paper presented at IS&T International Symposium on Electronic Imaging, 17-26 January, 2022, Digital Conference. Society for Imaging Sciences and Technology, 34, Article ID 156.
Open this publication in new window or tab >>Effect of hue shift towards robustness of convolutional neural networks
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Computer vision systems become deployed in diverse real time systems hence robustness is a major area of concern. As a vast majority of the AI enabled systems are based on convolutional neural networks based models which use 3-channel RGB images as input. It has been shown that the performance of AI systems, such as those used in classification, is impacted by distortions in the images. To date most work has been carried out on distortions such as noise, blur, compression. However, color related changes to images could also impact the performance. Therefore, the goal of this paper is to study the robustness of these models under different hue shifts.

Place, publisher, year, edition, pages
Society for Imaging Sciences and Technology, 2022
Series
IS&T International Symposium on Electronic Imaging Science and Technology, E-ISSN 2470-1173 ; 15
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-88937 (URN)10.2352/EI.2022.34.15.COLOR-156 (DOI)2-s2.0-85132406304 (Scopus ID)
Conference
IS&T International Symposium on Electronic Imaging, 17-26 January, 2022, Digital Conference
Available from: 2022-01-26 Created: 2022-01-26 Last updated: 2023-09-05Bibliographically approved
De, K. (2022). Exploring Effects of Colour and Image Quality in Semantic Segmentation by Deep Learning Methods. Paper presented at 30th Color and Imaging Conference 2022 (CIC30), Scottsdale, Arizona, November 13-17, 2022. Journal of Imaging Science and Technology, 66(5), 050401-1-050401-10, Article ID 050401.
Open this publication in new window or tab >>Exploring Effects of Colour and Image Quality in Semantic Segmentation by Deep Learning Methods
2022 (English)In: Journal of Imaging Science and Technology, ISSN 1062-3701, E-ISSN 1943-3522, Vol. 66, no 5, p. 050401-1-050401-10, article id 050401Article in journal (Refereed) Published
Abstract [en]

Recent advances in convolutional neural networks and vision transformers have brought about a revolution in the area of computer vision. Studies have shown that the performance of deep learning-based models is sensitive to image quality. The human visual system is trained to infer semantic information from poor quality images, but deep learning algorithms may find it challenging to perform this task. In this paper, we study the effect of image quality and color parameters on deep learning models trained for the task of semantic segmentation. One of the major challenges in benchmarking robust deep learning-based computer vision models is lack of challenging data covering different quality and colour parameters. In this paper, we have generated data using the subset of the standard benchmark semantic segmentation dataset (ADE20K) with the goal of studying the effect of different quality and colour parameters for the semantic segmentation task. To the best of our knowledge, this is one of the first attempts to benchmark semantic segmentation algorithms under different colour and quality parameters, and this study will motivate further research in this direction.

Place, publisher, year, edition, pages
The Society for Imaging Science and Technology, 2022
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-92695 (URN)10.2352/j.imagingsci.technol.2022.66.5.050401 (DOI)000915442700004 ()2-s2.0-85147138621 (Scopus ID)
Conference
30th Color and Imaging Conference 2022 (CIC30), Scottsdale, Arizona, November 13-17, 2022
Note

Validerad;2022;Nivå 2;2022-11-28 (sofila)

Available from: 2022-08-29 Created: 2022-08-29 Last updated: 2023-09-05Bibliographically approved
Swarup, V. S., Sadhya, D., Patel, V. & De, K. (2022). Presentation Attack Detection in Iris Recognition through Convolution Block Attention Module. In: 2022 IEEE International Joint Conference on Biometrics (IJCB): . Paper presented at 2022 IEEE International Joint Conference on Biometrics (IJCB), October 10-13, 2022, Abu Dhabi, United Arab Emirates. IEEE
Open this publication in new window or tab >>Presentation Attack Detection in Iris Recognition through Convolution Block Attention Module
2022 (English)In: 2022 IEEE International Joint Conference on Biometrics (IJCB), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Presentation Attacks (PAs) are a common spoofing mechanism in biometric authentications, especially iris-based models. The detection of these attacks is useful for distinguishing whether a sensor is presented with a live biometric or impersonated biometric through a recording, printout or spoof In recent studies, Convolutional Neural Networks have shown exceptional performance in detecting these attacks. In this paper, we propose an attention-based iris PA detection (PAD) termed d-CBAM that uses a convolution block attention mechanism introduced between the dense blocks of DenseNet. The core of this work is inspired by the use of DenseNet as a feature extractor i.e, feature maps from the last dense block are taken and passed to the attention maps. We have tested d-CBAM on the benchmark Clarkson, Notre Dame and NDCLD15 datasets, over which d-CBAM has shown better results in comparison to some traditional PAD solutions such as DenseNet, Spoof Net and Meta-Fusion. The error metrics (APCER and BPCER) were also noted to be competitive with the state-of-the-art.

Place, publisher, year, edition, pages
IEEE, 2022
Series
IEEE International Conference on Biometrics, Theory, Applications and Systems, ISSN 2474-9680, E-ISSN 2474-9699
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Machine Learning
Identifiers
urn:nbn:se:ltu:diva-95581 (URN)10.1109/IJCB54206.2022.10007966 (DOI)000926877700037 ()2-s2.0-85147254158 (Scopus ID)978-1-6654-6394-2 (ISBN)
Conference
2022 IEEE International Joint Conference on Biometrics (IJCB), October 10-13, 2022, Abu Dhabi, United Arab Emirates
Available from: 2023-02-10 Created: 2023-02-10 Last updated: 2024-03-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0221-8268

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