Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Panos P. Markopoulos"'
Publikováno v:
Sensors, Vol 24, Iss 18, p 6054 (2024)
Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the
Externí odkaz:
https://doaj.org/article/030beb2d1c694c33b5d5c74ca5f82594
Autor:
Manish Sharma, Mayur Dhanaraj, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, Eli Saber
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1497-1508 (2021)
Deep-learning object detection methods that are designed for computer vision applications tend to underperform when applied to remote sensing data. This is because contrary to computer vision, in remote sensing, training data are harder to collect an
Externí odkaz:
https://doaj.org/article/0f0551c073f049a3bdaaa03800c5a3e0
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2019, Iss 1, Pp 1-16 (2019)
Abstract Subspace-based direction-of-arrival (DoA) estimation commonly relies on the Principal-Component Analysis (PCA) of the sensor-array recorded snapshots. Therefore, it naturally inherits the sensitivity of PCA against outliers that may exist am
Externí odkaz:
https://doaj.org/article/dcf27220a9d74e45873024775aab74e9
Publikováno v:
IEEE Access, Vol 7, Pp 178454-178465 (2019)
Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-bas
Externí odkaz:
https://doaj.org/article/0f237e193f784f7eb3ba7c81adfb6e21
Autor:
Mayur Dhanaraj, Panos P. Markopoulos
Publikováno v:
IEEE Signal Processing Letters. 29:2343-2347
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83958d755053fae6011db3020bf42d26
https://doi.org/10.1109/lsp.2022.3205274
https://doi.org/10.1109/lsp.2022.3205274
Autor:
Duc H. Le, Panos P. Markopoulos
Publikováno v:
2022 IEEE Workshop on Signal Processing Systems (SiPS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::802066b1bdce2286f485f0431cfcd30d
https://doi.org/10.1109/sips55645.2022.9919215
https://doi.org/10.1109/sips55645.2022.9919215
Autor:
Mayur Dhanaraj, Panos P. Markopoulos
Publikováno v:
2022 56th Asilomar Conference on Signals, Systems, and Computers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b307c8f42369ffd4d0534e9c5d8bfa1f
https://doi.org/10.1109/ieeeconf56349.2022.10051902
https://doi.org/10.1109/ieeeconf56349.2022.10051902
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 15:587-602
Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning and data mining, among other fields. When tensor measurements arrive in a streaming fashion or are too many to jo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ef4d64a02e754ee4d4a46436b6b2dfe
https://doi.org/10.1109/jstsp.2021.3058846
https://doi.org/10.1109/jstsp.2021.3058846
Autor:
Eli Saber, Raymond Ptucha, Srivallabha Karnam, Mayur Dhanaraj, Panos P. Markopoulos, Dimitris G. Chachlakis, Manish Sharma
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1497-1508 (2021)
Deep-learning object detection methods that are designed for computer vision applications tend to underperform when applied to remote sensing data. This is because contrary to computer vision, in remote sensing, training data are harder to collect an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a60ee4f7da6bc147ad240cf61073aac9
https://ieeexplore.ieee.org/document/9273212/
https://ieeexplore.ieee.org/document/9273212/
Autor:
Panos P. Markopoulos, Mahsa Mozaffari
In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d04366de248566601178ff423323541c
https://doi.org/10.36227/techrxiv.17303801.v1
https://doi.org/10.36227/techrxiv.17303801.v1
