Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Mariana-Iuliana Georgescu"'
Publikováno v:
IEEE Access, Vol 8, Pp 49112-49124 (2020)
Computed Tomography (CT) scanners that are commonly-used in hospitals and medical centers nowadays produce low-resolution images, e.g. one voxel in the image corresponds to at most one-cubic millimeter of tissue. In order to accurately segment tumors
Externí odkaz:
https://doaj.org/article/a185496cacc44ff4949dadb69a4b5f88
Publikováno v:
IEEE Access, Vol 7, Pp 64827-64836 (2019)
We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve the state-of-the-art results in facial expression rec
Externí odkaz:
https://doaj.org/article/3c755ad2ff0f488f90543372938e3dc8
Autor:
Antonio Barbalau, Radu Tudor Ionescu, Mariana-Iuliana Georgescu, Jacob Dueholm, Bharathkumar Ramachandra, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas B. Moeslund, Mubarak Shah
Publikováno v:
Barbalau, A, Ionescu, R T, Georgescu, M-I, Dueholm, J V, Ramachandra, B, Nasrollahi, K, Shahbaz Khan, F, Moeslund, T B & shah, M 2023, ' SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection ', Computer Vision and Image Understanding, vol. 229, no. 103656, 103656 . https://doi.org/10.1016/j.cviu.2023.103656
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted the attention of many researchers. In this work, we revisit the self-s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4db485a8fd879fe8a2b1c43d337c87eb
https://vbn.aau.dk/da/publications/159a3c71-5b80-4d89-8db4-b7746e255365
https://vbn.aau.dk/da/publications/159a3c71-5b80-4d89-8db4-b7746e255365
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250552
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6950eefd36df4e4551c9ff9f35afdcaf
https://doi.org/10.1007/978-3-031-25056-9_22
https://doi.org/10.1007/978-3-031-25056-9_22
Publikováno v:
IEEE Access, Vol 8, Pp 49112-49124 (2020)
CT scanners that are commonly-used in hospitals nowadays produce low-resolution images, up to 512 pixels in size. One pixel in the image corresponds to a one millimeter piece of tissue. In order to accurately segment tumors and make treatment plans,
Publikováno v:
Machine Vision and Applications
We study a series of recognition tasks in two realistic scenarios requiring the analysis of faces under strong occlusion. On the one hand, we aim to recognize facial expressions of people wearing virtual reality headsets. On the other hand, we aim to
We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorpor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95c4a23182c1e0e4458d43b645b9836b
http://arxiv.org/abs/2108.07387
http://arxiv.org/abs/2108.07387
Publikováno v:
IEEE Access, Vol 7, Pp 64827-64836 (2019)
We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recogni
Autor:
Mubarak Shah, Mariana-Iuliana Georgescu, Marius Popescu, Fahad Shahbaz Khan, Radu Tudor Ionescu
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 44(9)
Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. The complexity of the task arises from the commonly-adopted definition of an abnormal event, that is, a rarely occurring
Publikováno v:
ICPR
In this paper, we study the task of facial expression recognition under strong occlusion. We are particularly interested in cases where 50% of the face is occluded, e.g. when the subject wears a Virtual Reality (VR) headset. While previous studies sh