Zobrazeno 1 - 10
of 1 154
pro vyhledávání: '"Cetintas"'
Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-scale datasets. Despite the importance of this task, there are currently
Externí odkaz:
http://arxiv.org/abs/2404.11426
Tracking objects over long videos effectively means solving a spectrum of problems, from short-term association for un-occluded objects to long-term association for objects that are occluded and then reappear in the scene. Methods tackling these two
Externí odkaz:
http://arxiv.org/abs/2212.03038
Autor:
Mert Erdin, Ceylan Polat, Teemu Smura, Sercan Irmak, Ortac Cetintas, Muhsin Cogal, Faruk Colak, Ahmet Karatas, Mustafa Sozen, Ferhat Matur, Olli Vapalahti, Tarja Sironen, Ibrahim Mehmet Ali Oktem
Publikováno v:
Emerging Infectious Diseases, Vol 30, Iss 4, Pp 779-782 (2024)
We report complete coding sequences of Orthohantavirus dobravaense (Dobrava virus) Igneada strains and phylogenetic characterization of all available complete coding sequences. Our analyses suggested separation of host-dependent lineages, followed by
Externí odkaz:
https://doaj.org/article/d7883ca2adc64ab6babd7d0cbf276396
Publikováno v:
Light: Science & Applications (2023)
Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-dis
Externí odkaz:
http://arxiv.org/abs/2208.03968
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) and Multiple Object Tracking and Segmentation (MOTS) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning methods, as defining a
Externí odkaz:
http://arxiv.org/abs/2207.07454
Autor:
Alwazeer, Duried, Bulut, Menekşe, Ceylan, Mehmet Murat, Çelebi, Yasemin, Kavrut, Enes, Çetintaş, Yunus, Tekin, Ali, Hayaloğlu, Ali Adnan
Publikováno v:
In LWT 15 August 2024 206
Autor:
Çelebi, Yasemin, Kavrut, Enes, Bulut, Menekşe, Çetintaş, Yunus, Tekin, Ali, Hayaloğlu, Ali Adnan, Alwazeer, Duried
Publikováno v:
In Food Chemistry 1 August 2024 448
Autor:
Cetintas, Ege, Luo, Yi, Nguyen, Charlene, Guo, Yuening, Li, Liqiao, Zhu, Yifang, Ozcan, Aydogan
Publikováno v:
Scientific Reports (2022)
The past decade marked a drastic increase in the usage of electronic cigarettes (e-cigs). The adverse health impact of secondhand exposure due to exhaled e-cig particles has raised significant concerns, demanding further research on the characteristi
Externí odkaz:
http://arxiv.org/abs/2109.10865
Autor:
Fabbri, Matteo, Braso, Guillem, Maugeri, Gianluca, Cetintas, Orcun, Gasparini, Riccardo, Osep, Aljosa, Calderara, Simone, Leal-Taixe, Laura, Cucchiara, Rita
Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance. However, data acquisition in crowded public environments raises data privacy concerns -- we are not allowed to
Externí odkaz:
http://arxiv.org/abs/2108.09518
Autor:
Luo, Yi, Zhao, Yifan, Li, Jingxi, Cetintas, Ege, Rivenson, Yair, Jarrahi, Mona, Ozcan, Aydogan
Publikováno v:
eLight (2022)
Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. We present a computer-free, all-optical image reconstruction method to see through random diffusers at
Externí odkaz:
http://arxiv.org/abs/2107.06586