Bilgisayar Mühendisliği Bölümü Bildiri / Sunu / Poster Koleksiyonu
https://hdl.handle.net/11655/413
2024-03-28T14:12:45ZA Plant Recognition Approach Using Shape and Color Features in Leaf Images
https://hdl.handle.net/11655/20014
A Plant Recognition Approach Using Shape and Color Features in Leaf Images
Caglayan, A; Guclu, O; Can, A.B.
2013-01-01T00:00:00ZCharacteristic Usage Of Turkish Internet Users In Office Environment
https://hdl.handle.net/11655/18669
Characteristic Usage Of Turkish Internet Users In Office Environment
Seydi, Keceli Ali; Kerem, Erzurumlu
In this paper, a lawyer office's network and internet traffic will be examined for determining the characteristics of users and network traffic load. In order to do that, some modifications on the lawyers office is made. Firstly, the switch which was previously used, replaced with a smart switch which can mirror traffic to a special port. Later, the office adsl modem is replaced with a modified Linux distribution. These linux device is used as a adsl modem/router for the network. This device also logged every incomming packet including the payload from it's both interfaces. After a months full run, the data gathered is collected and analized. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.
2011-01-01T00:00:00ZThe Visual Object Tracking Vot2016 Challenge Results
https://hdl.handle.net/11655/18659
The Visual Object Tracking Vot2016 Challenge Results
Kristan, Matej; Leonardis, Ales; Matas, Jiri; Felsberg, Michael; Pflugfelder, Roman; Cehovin, Luka; Vojir, Tomas; Hager, Gustav; Lukezic, Alan; Fernandez, Gustavo; Gupta, Abhinav; Petrosino, Alfredo; Memarmoghadam, Alireza; Garcia-Martin, Alvaro; Montero, Andres Solis; Vedaldi, Andrea; Robinson, Andreas; Ma, Andy J.; Varfolomieiev, Anton; Alatan, Aydin; Erdem, Aykut; Ghanem, Bernard; Liu, Bin; Han, Bohyung; Martinez, Brais; Chang, Chang-Ming; Xu, Changsheng; Sun, Chong; Kim, Daijin; Chen, Dapeng; Du, Dawei; Mishra, Deepak; Yeung, Dit-Yan; Gundogdu, Erhan; Erdem, Erkut; Khan, Fahad; Porikli, Fatih; Zhao, Fei; Bunyak, Filiz; Battistone, Francesco; Zhu, Gao; Roffo, Giorgio; Subrahmanyam, Gorthi R. K. Sai; Bastos, Guilherme; Seetharaman, Guna; Medeiros, Henry; Li, Hongdong; Qi, Honggang; Bischof, Horst; Possegger, Horst; Lu, Huchuan; Lee, Hyemin; Nam, Hyeonseob; Chang, Hyung Jin; Drummond, Isabela; Valmadre, Jack; Jeong, Jae-chan; Cho, Jae-il; Lee, Jae-Yeong; Zhu, Jianke; Feng, Jiayi; Gao, Jin; Choi, Jin Young; Xiao, Jingjing; Kim, Ji-Wan; Jeong, Jiyeoup; Henriques, Joao F.; Lang, Jochen; Choi, Jongwon; Martinez, Jose M.; Xing, Junliang; Gao, Junyu; Palaniappan, Kannappan; Lebeda, Karel; Gao, Ke; Mikolajczyk, Krystian; Qin, Lei; Wang, Lijun; Wen, Longyin; Bertinetto, Luca; Rapuru, Madan Kumar; Poostchi, Mahdieh; Maresca, Mario; Danelljan, Martin; Mueller, Matthias; Zhang, Mengdan; Arens, Michael; Valstar, Michel; Tang, Ming; Baek, Mooyeol; Khan, Muhammad Haris; Wang, Naiyan; Fan, Nana; Al-Shakarji, Noor; Miksik, Ondrej; Akin, Osman; Moallem, Payman; Senna, Pedro; Torr, Philip H. S.; Yuen, Pong C.; Huang, Qingming; Martin-Nieto, Rafael; Pelapur, Rengarajan; Bowden, Richard; Laganiere, Robert; Stolkin, Rustam; Walsh, Ryan; Krah, Sebastian B.; Li, Shengkun; Zhang, Shengping; Yao, Shizeng; Hadfield, Simon; Melzi, Simone; Lyu, Siwei; Li, Siyi; Becker, Stefan; Golodetz, Stuart; Kakanuru, Sumithra; Choi, Sunglok; Hu, Tao; Mauthner, Thomas; Zhang, Tianzhu; Pridmore, Tony; Santopietro, Vincenzo; Hu, Weiming; Li, Wenbo; Huebner, Wolfgang; Lan, Xiangyuan; Wang, Xiaomeng; Li, Xin; Li, Yang; Demiris, Yiannis; Wang, Yifan; Qi, Yuankai; Yuan, Zejian; Cai, Zexiong; Xu, Zhan; He, Zhenyu; Chi, Zhizhen
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.
2016-01-01T00:00:00ZThe Thermal Infrared Visual Object Tracking Vot-Tir2016 Challenge Results
https://hdl.handle.net/11655/18658
The Thermal Infrared Visual Object Tracking Vot-Tir2016 Challenge Results
Felsberg, Michael; Kristan, Matej; Matas, Jiri; Leonardis, Ales; Pflugfelder, Roman; Hager, Gustav; Berg, Amanda; Eldesokey, Abdelrahman; Ahlberg, Jorgen; Cehovin, Luka; Vojir, Tomas; Lukezic, Alan; Fernandez, Gustavo; Petrosino, Alfredo; Garcia-Martin, Alvaro; Montero, Andres Solis; Varfolomieiev, Anton; Erdem, Aykut; Han, Bohyung; Chang, Chang-Ming; Du, Dawei; Erdem, Erkut; Khan, Fahad Shahbaz; Porikli, Fatih; Zhao, Fei; Bunyak, Filiz; Battistone, Francesco; Zhu, Gao; Seetharaman, Guna; Li, Hongdong; Qi, Honggang; Bischof, Horst; Possegger, Horst; Nam, Hyeonseob; Valmadre, Jack; Zhu, Jianke; Feng, Jiayi; Lang, Jochen; Martinez, Jose M.; Palaniappan, Kannappan; Lebeda, Karel; Gao, Ke; Mikolajczyk, Krystian; Wen, Longyin; Bertinetto, Luca; Poostchi, Mahdieh; Maresca, Mario; Danelljan, Martin; Arens, Michael; Tang, Ming; Baek, Mooyeol; Fan, Nana; Al-Shakarji, Noor; Miksik, Ondrej; Akin, Osman; Torr, Philip H. S.; Huang, Qingming; Martin-Nieto, Rafael; Pelapur, Rengarajan; Bowden, Richard; Laganiere, Robert; Krah, Sebastian B.; Li, Shengkun; Yao, Shizeng; Hadfield, Simon; Lyu, Siwei; Becker, Stefan; Golodetz, Stuart; Hu, Tao; Mauthner, Thomas; Santopietro, Vincenzo; Li, Wenbo; Huebner, Wolfgang; Li, Xin; Li, Yang; Xu, Zhan; He, Zhenyu
The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.
2016-01-01T00:00:00Z