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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JMNFN5
Repositorysid.inpe.br/sibgrapi/2015/06.19.18.41
Last Update2015:06.19.18.41.46 (UTC) balbino@dcc.ufmg.br
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Metadata Last Update2020:02.19.02.14.03 (UTC) administrator
Citation KeyMesquitaNascCamp:2015:SiEsSu
TitleSimultaneously Estimation of Super-Resolution Images and Depth Maps from Low Resolution Sensors
FormatOn-line
Year2015
Access Date2021, Dec. 03
Number of Files1
Size1445 KiB
Context area
Author1 Mesquita, Daniel Balbino de
2 Nascimento, Erickson Rangel do
3 Campos, Mario Fernando Montenegro
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addressbalbino@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2015-06-19 18:41:46 :: balbino@dcc.ufmg.br -> administrator ::
2020-02-19 02:14:03 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsSuper-resolution
convex optimization
RGB-D data
3D reconstruction
computer vision
AbstractThe emergence of low cost sensors capable of providing texture and depth information of a scene is enabling the deployment of several applications such as gesture and object recognition and three-dimensional reconstruction of environments. However, commercially available sensors output low resolution data, which may not be suitable when more detailed information is necessary. With the purpose of increasing data resolution, at the same time reducing noise and filling the holes in the depth maps, in this work we propose a method that combines depth fusion and image reconstruction in a super-resolution framework. By joining low-resolution intensity images and depth maps in an optimization process, our methodology creates new images and depth maps of higher resolution and, at the same time, minimizes issues related with the absence of information (holes) in the depth map. Our experiments show that the proposed approach has increased the resolution of the images and depth maps without significant spawning of artifacts. Considering three different evaluation metrics, our methodology outperformed other three techniques commonly used to increase the resolution of combined images and depth maps acquired with low resolution, commercially available sensors.
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Languageen
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User Groupbalbino@dcc.ufmg.br
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Next Higher Units8JMKD3MGPBW34M/3K24PF8
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