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Joint object-material category segmentation from audio-visual cues

Abstract:

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials. In this paper, we therefore present an approach that augments the available dense visual cues with sparse auditory cues in order to estimate dense object and material labels. Since estimates of object class and material properties are mutually-informative, we optimise our multi-output labelling j...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.5244/C.29.40

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Technicolor More from this funder
Engineering and Physical Sciences Research Council More from this funder
Leverhulme Trust More from this funder
Publisher:
BMVA Press Publisher's website
Journal:
Proceedings of BMVC 2015 Journal website
Volume:
abs/1601.02220
Host title:
BMVC 2015: 26th British Machine Vision Conference
Publication date:
2015-09-10
DOI:
Source identifiers:
589583
Keywords:
Pubs id:
pubs:589583
UUID:
uuid:118d2199-a58e-45d3-9516-8c222ecd23e3
Local pid:
pubs:589583
Deposit date:
2016-04-03

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