Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Apostolos Axenopoulos"'
Autor:
Michael G. Strintzis, Dimitrios Tzovaras, Apostolos Axenopoulos, Petros Daras, Dimitrios Zarpalas
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2007 (2007)
This paper presents a novel methodology for content-based search and retrieval of 3D objects. After proper positioning of the 3D objects using translation and scaling, a set of functionals is applied to the 3D model producing a new domain of concentr
Externí odkaz:
https://doaj.org/article/230fec0a7bd54f57b62b90a55bcc067a
Autor:
Luca Gagliardi, Andrea Raffo, Ulderico Fugacci, Silvia Biasotti, Walter Rocchia, Hao Huang, Boulbaba Ben Amor, Yi Fang, Yuanyuan Zhang, Xiao Wang, Charles Christoffer, Daisuke Kihara, Apostolos Axenopoulos, Stelios Mylonas, Petros Daras
Publikováno v:
Computers & Graphics. 107:20-31
Autor:
Georgios Kalitsios, Lazaros Lazaridis, Athanasios Psaltis, Apostolos Axenopoulos, Petros Daras
Publikováno v:
2022 4th International Conference on Robotics and Computer Vision (ICRCV).
Autor:
Spyros Petrakis, Stelios K. Mylonas, Sotiris Katsamakas, Kostas Stamatopoulos, Apostolos Axenopoulos, Ioannis Gkekas, Petros Daras
Publikováno v:
BIBE
2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)
2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE)
Drug discovery involves extremely costly and time consuming procedures and can be significantly benefited by computational approaches, such as virtual screening (VS). Structure-based VS relies on scoring functions which aim to evaluate the binding of
Publikováno v:
Bioinformatics (Oxford, England).
Motivation The knowledge of potentially druggable binding sites on proteins is an important preliminary step toward the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the
Publikováno v:
Intelligent Systems Reference Library ISBN: 9783030427481
The main goal of this chapter is to develop a system for automatic protein classification. Proteins are classified using CNNs trained on ImageNet, which are tuned using a set of multiview 2D images of 3D protein structures generated by Jmol, which is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::daeed9d79baf756e83661386e8d454f6
https://doi.org/10.1007/978-3-030-42750-4_9
https://doi.org/10.1007/978-3-030-42750-4_9
Autor:
Ziming Liu, Weiping Yu, Zehui Gong, Yan Ding, Fanman Meng, Qizhang Lin, Dheeraj Reddy Pailla, Hongliang Li, Yan Luo, Pengfei Zhu, Murari Mandal, Zhiwei Wei, Junwen Pan, Apostolos Axenopoulos, Mubarak Shah, Michael Schleiss, Jong Hwan Ko, Qinghua Hu, Yongwoo Kim, Sai Wang, Hansheng Chen, Heng Fan, Zichen Song, Chengzhen Duan, Xiaogang Jia, Haibin Ling, Ming-Hsuan Yang, Jungyeop Yoo, Qiu Shi, Hao Zhou, Bin Dong, Xingjie Zhao, Athanasios Psaltis, Chen Chen, Zhongjie Fan, Wenxiang Lin, Yuehan Yao, Joochan Lee, Pratik Narang, Yu Sun, Weida Qin, Sarvesh Mehta, Qiong Liu, Guosheng Zhang, Zhenyu Xu, Petros Daras, Minjian Zhang, Longyin Wen, Jun Yu, Guangyu Gao, Yuyao Huang, Lu Xiong, Jingkai Zhou, Mingyu Liu, Xi Zhao, Yang Xiao, Xuanxin Liu, Yi Wang, Heqian Qiu, Chongyang Zhang, Lars Sommer, Taijin Zhao, Faizan Farooq Khan, Wei Tian, Jincai Cui, Yingjie Liu, Shuai Li, Zhiguo Cao, Shuqin Huang, Ting Sun, Haonian Xie, Ioannis Athanasiadis, Zhipeng Luo, Dawei Du, Wei Guo, Rohit Ramaprasad, Xin He, Sungtae Moon, Arne Schumann, Ayush Jain, Changlin Li, Dong Yin, Daniel Stadler
Publikováno v:
Computer Vision – ECCV 2020 Workshops ISBN: 9783030668228
ECCV Workshops (4)
ECCV Workshops (4)
The Vision Meets Drone Object Detection in Image Challenge (VisDrone-DET 2020) is the third annual object detector benchmarking activity. Compared with the previous VisDrone-DET 2018 and VisDrone-DET 2019 challenges, many submitted object detectors e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::609bce22589ccd0e26af40d7785642be
https://doi.org/10.1007/978-3-030-66823-5_42
https://doi.org/10.1007/978-3-030-66823-5_42
Autor:
Florent Langenfeld, David Hunter, Matthieu Montes, Karim Hammoudi, Daisuke Kihara, Feryal Windal, Yu-Kun Lai, Ekpo Otu, Paul L. Rosin, Stelios K. Mylonas, Petros Daras, Apostolos Axenopoulos, Halim Benhabiles, Reyer Zwiggelaar, Andrea Giachetti, Charles Christoffer, Adnane Cabani, Tunde Aderinwale, Yuxu Peng, Yonghuai Liu, Mahmoud Melkemi, Genki Terashi
Publikováno v:
Computers and Graphics
Computers and Graphics, Elsevier, 2020, 91, pp.189-198. ⟨10.1016/j.cag.2020.07.013⟩
Computers & Graphics
Computers and Graphics, 2020, 91, pp.189-198. ⟨10.1016/j.cag.2020.07.013⟩
Computers and Graphics, Elsevier, 2020, 91, pp.189-198. ⟨10.1016/j.cag.2020.07.013⟩
Computers & Graphics
Computers and Graphics, 2020, 91, pp.189-198. ⟨10.1016/j.cag.2020.07.013⟩
[#17491] article suite à une conférence orale: 13th EG Euroworkshop on 3D object retrieval, 3DOR 2020, Graz, Austria, september 4-5, 2020; International audience; Proteins are natural modular objects usually composed of several domains, each domain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca77de2112dc6449e41847eefc6ba5d9
http://hdl.handle.net/11562/1030083
http://hdl.handle.net/11562/1030083
Publikováno v:
Biomimetic and Biohybrid Systems ISBN: 9783030643126
Living Machines
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Biomimetic and Biohybrid Systems
Biomimetic and Biohybrid Systems-9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings
Living Machines
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Biomimetic and Biohybrid Systems
Biomimetic and Biohybrid Systems-9th International Conference, Living Machines 2020, Freiburg, Germany, July 28–30, 2020, Proceedings
In the current study, a region-based approach for object detection is presented that is suitable for handling very small objects and objects in low-resolution images. To address this challenge, an anchoring mechanism for the region proposal stage of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c87842daf19c1c366c1ee6fc706206d6
https://doi.org/10.1007/978-3-030-64313-3_3
https://doi.org/10.1007/978-3-030-64313-3_3
Publikováno v:
ICE/ITMC
Deep learning approaches have recently proven their effectiveness in the task of image Super Resolution (SR). In most cases, very deep structures have been adopted to increase the models’ performance, leading to neural networks with a high paramete