Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Kokul, Thanikasalam"'
One-stream Transformer trackers have shown outstanding performance in challenging benchmark datasets over the last three years, as they enable interaction between the target template and search region tokens to extract target-oriented features with m
Externí odkaz:
http://arxiv.org/abs/2402.08195
Publikováno v:
IEEE Access 2023
Single-object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-bas
Externí odkaz:
http://arxiv.org/abs/2302.11867
Autor:
Anparasy Sivaanpu, Kumaradevan Punithakumar, Kokul Thanikasalam, Michelle Noga, Rui Zheng, Dean Ta, Edmond H.M. Lou, Lawrence H. Le
Publikováno v:
Informatics in Medicine Unlocked, Vol 50, Iss , Pp 101569- (2024)
The quality of ultrasound (US) imaging has been constrained by its limited contrast and resolution, inherent speckle noise, and the presence of other artifacts. Existing traditional and deep learning-based US denoising approaches have many limitation
Externí odkaz:
https://doaj.org/article/379920f6833e49bb8956784b7bf8fda1
Akademický článek
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Autor:
Anparasy Sivaanpu, Kokul Thanikasalam
Publikováno v:
2022 7th International Conference on Information Technology Research (ICITR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57361cf877f73f8b4ad0d6613ed4e292
https://doi.org/10.1109/icitr57877.2022.9993091
https://doi.org/10.1109/icitr57877.2022.9993091
Publikováno v:
2022 7th International Conference on Information Technology Research (ICITR).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f20855510972c79cf34e835ba0a89d2b
https://doi.org/10.1109/icitr57877.2022.9993277
https://doi.org/10.1109/icitr57877.2022.9993277
Autor:
Subaramya Srivishagan, Logiraj Kumaralingam, Kokul Thanikasalam, U.A.J. Pinidiyaarachchi, Nagulan Ratnarajah
Publikováno v:
Psychiatry research. Neuroimaging. 328
Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5c5ce21a954e488b2c58cc526a989a9
https://pubmed.ncbi.nlm.nih.gov/36495726
https://pubmed.ncbi.nlm.nih.gov/36495726
Autor:
Anparasy Sivaanpu, Kokul Thanikasalam
Publikováno v:
International Journal on Advances in ICT for Emerging Regions (ICTer). 14:1-12
Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy image. Performances of the pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f683193fa6f31ced8f2ac232020b9622
https://doi.org/10.4038/icter.v14i3.7219
https://doi.org/10.4038/icter.v14i3.7219
Autor:
Amirthalingam Ramanan, Sridha Sridharan, Clinton Fookes, Kokul Thanikasalam, Amalka Pinidiyaarachchi
Publikováno v:
IEEE Transactions on Information Forensics and Security. 15:1276-1289
Deep similarity trackers are able to track above real-time speed. However, their accuracy is considerably lower than deep classification based trackers since they avoid valuable online cues. To feed the target-specific information for real-time objec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7264179683a030fc2ddf8cda3deeeaf5
https://doi.org/10.1109/tifs.2019.2935871
https://doi.org/10.1109/tifs.2019.2935871
Autor:
Logiraj Kumaralingam, Kokul Thanikasalam, Sittampalam Sotheeswaran, Jeyasuthan Mahadevan, Nagulan Ratnarajah
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164309
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::545f4e78077aa3d99ab6438a042789e1
https://doi.org/10.1007/978-3-031-16431-6_18
https://doi.org/10.1007/978-3-031-16431-6_18