Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Benson Mbani"'
Autor:
Benson Mbani, Jens Greinert
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-8 (2023)
Abstract We provide a sequence of analysis-ready optical underwater images from the Clarion-Clipperton Zone (CCZ) of the Pacific Ocean. The images were originally recorded using a towed camera sledge that photographed a seabed covered with polymetall
Externí odkaz:
https://doaj.org/article/9ded40f4cc044dc29be60045f422aa50
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-16 (2023)
Abstract Recent advances in optical underwater imaging technologies enable the acquisition of huge numbers of high-resolution seafloor images during scientific expeditions. While these images contain valuable information for non-invasive monitoring o
Externí odkaz:
https://doaj.org/article/e74de9105cf54faca09bc0f05be18cb5
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-20 (2022)
Abstract Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically base
Externí odkaz:
https://doaj.org/article/d1dc3d3737fa41bf82a6073315f61c38
The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2c4a475b4c3447d3010796923e98c47
https://doi.org/10.3289/sw_2_2023
https://doi.org/10.3289/sw_2_2023
This is an A.I. - based workflow for detecting megabenthic fauna from a sequence of underwater optical images. The workflow (semi) automatically generates weak annotations through the analysis of superpixels, and uses these (refined and semantically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7abdfc37820f59672350a868a2cb8b90
https://doi.org/10.3289/sw_1_2023
https://doi.org/10.3289/sw_1_2023