Zobrazeno 1 - 10
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pro vyhledávání: '"Isikdogan A"'
Autor:
Isikdogan, Leo F, Nayak, Bhavin V, Wu, Chyuan-Tyng, Moreira, Joao Peralta, Rao, Sushma, Michael, Gilad
We propose a system comprised of fixed-topology neural networks having partially frozen weights, named SemifreddoNets. SemifreddoNets work as fully-pipelined hardware blocks that are optimized to have an efficient hardware implementation. Those block
Externí odkaz:
http://arxiv.org/abs/2006.06888
We propose fixed-function neural network hardware that is designed to perform pixel-to-pixel image transformations in a highly efficient way. We use a fully trainable, fixed-topology neural network to build a model that can perform a wide variety of
Externí odkaz:
http://arxiv.org/abs/2001.00630
Autor:
Can, Canan, Akdeniz, Nadiye, Kömek, Halil *, Gündoğan, Cihan, Urakçı, Zuhat, Işıkdoğan, Abdurrahman
Publikováno v:
In Revista Española de Medicina Nuclear e Imagen Molecular (English Edition) January-February 2022 41(1):3-10
Autor:
Wu, Chyuan-Tyng, Isikdogan, Leo F., Rao, Sushma, Nayak, Bhavin, Gerasimow, Timo, Sutic, Aleksandar, Ain-kedem, Liron, Michael, Gilad
Publikováno v:
IEEE International Conference on Image Processing (ICIP), 2019, pp. 4624-4628
Traditional image signal processors (ISPs) are primarily designed and optimized to improve the image quality perceived by humans. However, optimal perceptual image quality does not always translate into optimal performance for computer vision applica
Externí odkaz:
http://arxiv.org/abs/1911.05931
In a typical video conferencing setup, it is hard to maintain eye contact during a call since it requires looking into the camera rather than the display. We propose an eye contact correction model that restores the eye contact regardless of the rela
Externí odkaz:
http://arxiv.org/abs/1906.05378
Autor:
Isikdogan, A., Sakin, A., Tural, D., Cubukcu, E., Bozkurt, O., Harputluoglu, H., Kefeli, U., Alacacioglu, A., Geredeli, C., Cicin, I., Artac, M., Karabulut, B., Sendur, M., Bilir, C., Turk, H., YALÇIN, SIDDIKA SONGÜL, GÜMÜŞ, MAHMUT, Karadurmus, N., Arslan, C., Cevik, D., Dane, F., Cil, T.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______9651::028cb7ccc65fb5b27dbc379fc3b274ad
http://hdl.handle.net/20.500.12645/30868
http://hdl.handle.net/20.500.12645/30868
Akademický článek
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Autor:
Abdurrahman Isikdogan, Mustafa Karaca, Mehmet Nahit Sendur, Mahmut Gumus, Ahmet Alacacioglu, Duygu Cevik, Ahmet Bilici, Faysal Dane, Eda Tanrikulu Simsek, Cagatay Arslan, Nuri Karadurmus, Bulent Karabulut, Cemil Bilir, Mehmet Artac, Mahmut Emre Yildirim, Suayib Yalcin, Umut Kefeli, Irfan Cicin, Haci Mehmet Turk, Erdem Cubukcu
[No Abstract Available]
Amgen Ilac Tic Ltd Sti, Turkey
Amgen Ilac Tic Ltd Sti, Turkey
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::193e4292ea8e82a0367a9bc93f405f58
https://ascopubs.org/doi/10.1200/JCO.2021.39.15_suppl.e15561
https://ascopubs.org/doi/10.1200/JCO.2021.39.15_suppl.e15561
Publikováno v:
IEEE Geoscience and Remote Sensing Letters 12/11 (2015): 2218-2221
Quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose a new method that extracts channels from remotely sensed images and estimates their widths. O
Externí odkaz:
http://arxiv.org/abs/1506.08670
Akademický článek
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