Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Joakim Bruslund Haurum"'
Publikováno v:
Water, Vol 12, Iss 12, p 3412 (2020)
Sewer pipe inspections are currently conducted by professionals who remotely control a robot from above ground. This expensive and slow approach is prone to human mistakes. Therefore, there is both an economic and scientific interest in automating th
Externí odkaz:
https://doaj.org/article/a73248682d404484969351d025c3e30d
Publikováno v:
Haurum, J B, Madadi, M, Guerrero, S E & Moeslund, T B 2022, ' Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification ', Automation in Construction, vol. 144, 104614 . https://doi.org/10.1016/j.autcon.2022.104614
A crucial part of image classification consists of capturing non-local spatial semantics of image content. This paper describes the multi-scale hybrid vision transformer (MSHViT), an extension of the classical convolutional neural network (CNN) backb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::698a8c11f40098b4308d0cd4b2a98dde
https://vbn.aau.dk/ws/files/492073391/1_s2.0_S0926580522004848_main.pdf
https://vbn.aau.dk/ws/files/492073391/1_s2.0_S0926580522004848_main.pdf
Publikováno v:
Proceedings of the Northern Lights Deep Learning Workshop; Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022
We present the first work where re-identification ofthe Giant Sunfish (Mola alexandrini) is automated using computer vision and deep learning. We propose a pipeline that scores an mAP of 60.34% on a full rank of the novel TinyMola dataset which inclu
Publikováno v:
Haurum, J B, Madadi, M, Guerrero, S E & Moeslund, T B 2022, Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder . in Proceedings-2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 . IEEE, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1441-1452, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, United States, 04/01/2022 . https://doi.org/10.1109/WACV51458.2022.00151
The sewerage infrastructure is one of the most important and expensive infrastructures in modern society. In order to efficiently manage the sewerage infrastructure, automated sewer inspection has to be utilized. However, while sewer defect classific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab37967cac0d237492b34250393173a1
https://vbn.aau.dk/da/publications/158a993d-1e79-49b8-b760-ba1fec50d5d9
https://vbn.aau.dk/da/publications/158a993d-1e79-49b8-b760-ba1fec50d5d9
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200731
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34ccf4ad5b652b3742d4ff481778c536
https://doi.org/10.1007/978-3-031-20074-8_2
https://doi.org/10.1007/978-3-031-20074-8_2
Autor:
Thomas B. Moeslund, Moaaz Mohamed Jamal Allahham, Ivan Adriyanov Nikolov, Kasper Schøn Henriksen, Mathias Stougaard Lynge, Joakim Bruslund Haurum
Publikováno v:
Haurum, J B, Allahham, M M J, Lynge, M S, Henriksen, K S, Nikolov, I A & Moeslund, T B 2021, Sewer Defect Classification using Synthetic Point Clouds . in G M Farinella, P Radeva, J Braz & K Bouatouch (eds), Proceedings of the 16th International Conference on Computer Vision Theory and Applications (VISAPP) . vol. 5, SCITEPRESS Digital Library, pp. 891-900, International Conference on Computer Vision Theory and Applications, 08/02/2021 . https://doi.org/10.5220/0010207908910900
VISIGRAPP (5: VISAPP)
VISIGRAPP (5: VISAPP)
Sewer pipes are currently manually inspected by trained inspectors, making the process prone to human errors, which can be potentially critical. There is therefore a great research and industry interest in automating the sewer inspection process. Pre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b468c7e51532cf2533a73b3158aa696c
https://vbn.aau.dk/da/publications/47e56ed4-d242-4570-98a6-408bacd66176
https://vbn.aau.dk/da/publications/47e56ed4-d242-4570-98a6-408bacd66176
Publikováno v:
Water
Volume 12
Issue 12
Haurum, J B, Bahnsen, C H, Pedersen, M & Moeslund, T B 2020, ' Water Level Estimation in Sewer Pipes using Deep Convolutional Neural Networks ', Water, vol. 12, no. 12, 3412 . https://doi.org/10.3390/w12123412
Water, Vol 12, Iss 3412, p 3412 (2020)
Volume 12
Issue 12
Haurum, J B, Bahnsen, C H, Pedersen, M & Moeslund, T B 2020, ' Water Level Estimation in Sewer Pipes using Deep Convolutional Neural Networks ', Water, vol. 12, no. 12, 3412 . https://doi.org/10.3390/w12123412
Water, Vol 12, Iss 3412, p 3412 (2020)
Sewer pipe inspections are currently conducted by professionals who remotely control a robot from above ground. This expensive and slow approach is prone to human mistakes. Therefore, there is both an economic and scientific interest in automating th
Publikováno v:
Pedersen, M, Haurum, J B, Bengtson, S H & Moeslund, T B 2020, 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset . in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), I E E E Conference on Computer Vision and Pattern Recognition. Proceedings, pp. 2426-2436, 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, United States, 14/06/2020 . https://doi.org/10.1109/CVPR42600.2020.00250
CVPR
CVPR
In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF. Zebrafish is an increasingly popular model organism used for studying neurological disorders, drug addiction, and more.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c81575595b353f24c40f1933fe9378c7
https://vbn.aau.dk/da/publications/1b1ce9ee-7f73-4f5d-b1a7-f7ec8fbd3820
https://vbn.aau.dk/da/publications/1b1ce9ee-7f73-4f5d-b1a7-f7ec8fbd3820
Autor:
Anastasija Karpova, Stefan Hein Bengtson, Joakim Bruslund Haurum, Thomas B. Moeslund, Malte Pedersen
Publikováno v:
Haurum, J B, Karpova, A, Pedersen, M, Bengtson, S H & Moeslund, T B 2020, Re-Identification of Zebrafish using Metric Learning . in Proceedings-2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020 ., 9096922, IEEE, pp. 1-11, 2020 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW), Aspen, Colorado, United States, 01/03/2020 . https://doi.org/10.1109/WACVW50321.2020.9096922
WACV Workshops
WACV Workshops
Zebrafish are widely used for drug development and behavioral pattern studies. The currently employed zebrafish re-identification methods rely solely on top-view and grayscale images which require a significant amount of annotated data in order to pe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06731af5e650d3a62ed67befb0d10d6e
https://vbn.aau.dk/ws/files/322581943/Haurum_Re_Identification_of_Zebrafish_using_Metric_Learning_WACVW_2020_paper.pdf
https://vbn.aau.dk/ws/files/322581943/Haurum_Re_Identification_of_Zebrafish_using_Metric_Learning_WACVW_2020_paper.pdf
Publikováno v:
Haurum, J B & Moeslund, T B 2020, ' A Survey on Image-Based Automation of CCTV and SSET Sewer Inspections ', Automation in Construction, vol. 111, 103061 . https://doi.org/10.1016/j.autcon.2019.103061
This survey presents an in-depth overview of the last 25 years of research within the field of image-based automation of Closed-Circuit Television (CCTV) and Sewer Scanner and Evaluation Technology (SSET) sewer inspection. The survey investigates bot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5366dda3e64f2f7720abe911045c33c5
https://vbn.aau.dk/da/publications/c94e4044-b7c7-43d1-b193-813ce82ad452
https://vbn.aau.dk/da/publications/c94e4044-b7c7-43d1-b193-813ce82ad452