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of 312
pro vyhledávání: '"Zust, A."'
Autor:
Duisterhof, Bardienus, Zust, Lojze, Weinzaepfel, Philippe, Leroy, Vincent, Cabon, Yohann, Revaud, Jerome
Structure-from-Motion (SfM), a task aiming at jointly recovering camera poses and 3D geometry of a scene given a set of images, remains a hard problem with still many open challenges despite decades of significant progress. The traditional solution f
Externí odkaz:
http://arxiv.org/abs/2409.19152
The progress in maritime obstacle detection is hindered by the lack of a diverse dataset that adequately captures the complexity of general maritime environments. We present the first maritime panoptic obstacle detection benchmark LaRS, featuring sce
Externí odkaz:
http://arxiv.org/abs/2308.09618
Maritime obstacle detection is critical for safe navigation of autonomous surface vehicles (ASVs). While the accuracy of image-based detection methods has advanced substantially, their computational and memory requirements prohibit deployment on embe
Externí odkaz:
http://arxiv.org/abs/2304.11249
Autor:
Kiefer, Benjamin, Kristan, Matej, Perš, Janez, Žust, Lojze, Poiesi, Fabio, Andrade, Fabio Augusto de Alcantara, Bernardino, Alexandre, Dawkins, Matthew, Raitoharju, Jenni, Quan, Yitong, Atmaca, Adem, Höfer, Timon, Zhang, Qiming, Xu, Yufei, Zhang, Jing, Tao, Dacheng, Sommer, Lars, Spraul, Raphael, Zhao, Hangyue, Zhang, Hongpu, Zhao, Yanyun, Augustin, Jan Lukas, Jeon, Eui-ik, Lee, Impyeong, Zedda, Luca, Loddo, Andrea, Di Ruberto, Cecilia, Verma, Sagar, Gupta, Siddharth, Muralidhara, Shishir, Hegde, Niharika, Xing, Daitao, Evangeliou, Nikolaos, Tzes, Anthony, Bartl, Vojtěch, Špaňhel, Jakub, Herout, Adam, Bhowmik, Neelanjan, Breckon, Toby P., Kundargi, Shivanand, Anvekar, Tejas, Desai, Chaitra, Tabib, Ramesh Ashok, Mudengudi, Uma, Vats, Arpita, Song, Yang, Liu, Delong, Li, Yonglin, Li, Shuman, Tan, Chenhao, Lan, Long, Somers, Vladimir, De Vleeschouwer, Christophe, Alahi, Alexandre, Huang, Hsiang-Wei, Yang, Cheng-Yen, Hwang, Jenq-Neng, Kim, Pyong-Kun, Kim, Kwangju, Lee, Kyoungoh, Jiang, Shuai, Li, Haiwen, Ziqiang, Zheng, Vu, Tuan-Anh, Nguyen-Truong, Hai, Yeung, Sai-Kit, Jia, Zhuang, Yang, Sophia, Hsu, Chih-Chung, Hou, Xiu-Yu, Jhang, Yu-An, Yang, Simon, Yang, Mau-Tsuen
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritim
Externí odkaz:
http://arxiv.org/abs/2211.13508
Autor:
Žust, Lojze, Kristan, Matej
Publikováno v:
Sensors 2022, 22, 9139
Robust maritime obstacle detection is critical for safe navigation of autonomous boats and timely collision avoidance. The current state-of-the-art is based on deep segmentation networks trained on large datasets. However, per-pixel ground truth labe
Externí odkaz:
http://arxiv.org/abs/2206.13263
Autor:
Žust, Lojze, Kristan, Matej
Robust maritime obstacle detection is essential for fully autonomous unmanned surface vehicles (USVs). The currently widely adopted segmentation-based obstacle detection methods are prone to misclassification of object reflections and sun glitter as
Externí odkaz:
http://arxiv.org/abs/2203.05352
Autor:
Hribar, Jernej1 Jernej.Hribar@ijs.si, Zust, Martin1
Publikováno v:
Electrotechnical Review / Elektrotehniski Vestnik. 2024, Vol. 91 Issue 4, p203-209. 7p.
Autor:
Žust, Lojze, Kristan, Matej
Coastal water autonomous boats rely on robust perception methods for obstacle detection and timely collision avoidance. The current state-of-the-art is based on deep segmentation networks trained on large datasets. Per-pixel ground truth labeling of
Externí odkaz:
http://arxiv.org/abs/2108.00564
Akademický článek
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Autor:
Schuler, Luc1 (AUTHOR) luc.schuler@alva.etat.lu, Zust, Danny1 (AUTHOR), Dahm, Georges2 (AUTHOR), Clabots, Fabienne1 (AUTHOR)
Publikováno v:
Food Additives & Contaminants: Part B: Surveillance Communications. Dec2023, Vol. 16 Issue 4, p350-360. 11p.