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http://hdl.handle.net/1942/39309
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | VANHERLE, Bram | - |
dc.contributor.author | MOONEN, Steven | - |
dc.contributor.author | VAN REETH, Frank | - |
dc.contributor.author | MICHIELS, Nick | - |
dc.date.accessioned | 2023-01-23T15:46:46Z | - |
dc.date.available | 2023-01-23T15:46:46Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2023-01-16T09:59:08Z | - |
dc.identifier.citation | British Machine Vision Conference, London, 21 - 24 november 2022 | - |
dc.identifier.uri | http://hdl.handle.net/1942/39309 | - |
dc.language.iso | en | - |
dc.publisher | BMVA Press | - |
dc.title | Analysis of Training Object Detection Models with Synthetic Data | - |
dc.type | Conference Material | - |
local.bibliographicCitation.conferencedate | 21 - 24 november 2022 | - |
local.bibliographicCitation.conferencename | British Machine Vision Conference | - |
local.bibliographicCitation.conferenceplace | London | - |
local.format.pages | 1 | - |
local.bibliographicCitation.jcat | C2 | - |
local.publisher.place | London | - |
local.type.refereed | Refereed | - |
local.type.specified | Conference Poster | - |
dc.identifier.url | https://bmvc2022.mpi-inf.mpg.de/0833_poster.pdf | - |
local.provider.type | - | |
local.bibliographicCitation.btitle | Proceedings of the 33rd British Machine Vision Conference | - |
local.uhasselt.international | no | - |
item.fullcitation | VANHERLE, Bram; MOONEN, Steven; VAN REETH, Frank & MICHIELS, Nick (2022) Analysis of Training Object Detection Models with Synthetic Data. In: British Machine Vision Conference, London, 21 - 24 november 2022. | - |
item.contributor | VANHERLE, Bram | - |
item.contributor | MOONEN, Steven | - |
item.contributor | VAN REETH, Frank | - |
item.contributor | MICHIELS, Nick | - |
item.accessRights | Restricted Access | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
0833_poster.pdf Restricted Access | Conference material | 906.92 kB | Adobe PDF | View/Open Request a copy |
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