Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Parthipan Siva"'
Autor:
Parthipan Siva, Alexander Wong, Patricia Hewston, George Ioannidis, Jonathan Adachi, Alexander Rabinovich, Andrea W. Lee, Alexandra Papaioannou
Publikováno v:
Sensors, Vol 24, Iss 4, p 1056 (2024)
With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place, predicting risk factors such as falls and hospitalization and providing early interve
Externí odkaz:
https://doaj.org/article/ac1b790cfba74312a4250184b69f9f35
Autor:
Hugo Masson, Amran Bhuiyan, Le Thanh Nguyen-Meidine, Mehrsan Javan, Parthipan Siva, Ismail Ben Ayed, Eric Granger
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2021, Iss 1, Pp 1-31 (2021)
Abstract Recent years have witnessed a substantial increase in the deep learning (DL) architectures proposed for visual recognition tasks like person re-identification, where individuals must be recognized over multiple distributed cameras. Although
Externí odkaz:
https://doaj.org/article/7751645e4e1b42aab28febe025bf1bf4
Publikováno v:
IEEE Access, Vol 4, Pp 1915-1924 (2016)
Deep neural networks are a branch in machine learning that has seen a meteoric rise in popularity due to its powerful abilities to represent and model high-level abstractions in highly complex data. One area in deep neural networks that are ripe for
Externí odkaz:
https://doaj.org/article/6cdcedecfccf4f71b15b25fd9f504303
Publikováno v:
IEEE Access, Vol 3, Pp 469-477 (2015)
There has been significant interest in the use of fully connected graphical models and deep-structured graphical models for the purpose of structured inference. However, fully connected and deep-structured graphical models have been largely explored
Externí odkaz:
https://doaj.org/article/de34ae40edb94d21a1b0d044a4afd509
Publikováno v:
Psychology of Aesthetics, Creativity, and the Arts. 15:746-757
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a221d86f579b56123f2832fb3c5b50ae
https://doi.org/10.1037/aca0000345
https://doi.org/10.1037/aca0000345
Autor:
Feng Shi, Paul Marchwica, Juan Camilo Gamboa Higuera, Mike Jamieson, Mehrsan Javan, Parthipan Siva
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36bd58ac768f3983ed3ec8a73b7db206
https://doi.org/10.1109/wacv51458.2022.00382
https://doi.org/10.1109/wacv51458.2022.00382
Publikováno v:
WACV
Person re-identification is an important yet challenging problem in visual recognition. Despite the recent advances with deep learning (DL) models for spatio-temporal and multi-modal fusion, re-identification approaches often fail to leverage the con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::795dac3b8ac4a7e27db71c78afd2e438
https://doi.org/10.1109/wacv45572.2020.9093370
https://doi.org/10.1109/wacv45572.2020.9093370
Publikováno v:
WACV
An ongoing major challenge in computer vision is the task of person re-identification, where the goal is to match individuals across different, non-overlapping camera views. While recent success has been achieved via supervised learning using deep ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4c6de9ad11c9a50c8e274424cac1e59
http://arxiv.org/abs/2001.04928
http://arxiv.org/abs/2001.04928
Publikováno v:
Journal of Signal Processing Systems. 90:931-946
Deep neural networks (DNNs) have shown significant promise in different fields including computer vision. Although previous research have demonstrated the ability of DNNs, utilizing these types of networks for real-time applications on embedded syste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e286df43b960eb38b1d3469bf26eeb63
https://doi.org/10.1007/s11265-017-1265-3
https://doi.org/10.1007/s11265-017-1265-3
Autor:
Mohammad Javad Shafiee, Christian Scharfenberger, Parthipan Siva, Alexander Wong, Paul Fieguth
Publikováno v:
IEEE Signal Processing Letters. 23:1404-1408
In this letter, a novel approach to visual saliency detection via neural response divergence (NeRD) is proposed, where synaptic portions of deep neural networks, previously trained for complex object recognition, are leveraged to compute low-level cu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::16e82de862a7b4017a1134c6fb1057ac
https://doi.org/10.1109/lsp.2016.2600592
https://doi.org/10.1109/lsp.2016.2600592