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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JMNFN5
Repositorysid.inpe.br/sibgrapi/2015/06.19.18.41
Last Update2015:06.19.18.41.46 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.19.18.41.46
Metadata Last Update2022:06.14.00.08.05 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.43
Citation KeyMesquitaNascCamp:2015:SiEsSu
TitleSimultaneously Estimation of Super-Resolution Images and Depth Maps from Low Resolution Sensors
FormatOn-line
Year2015
Access Date2024, Mar. 28
Number of Files1
Size1445 KiB
2. Context
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, BA, Brazil
Date26-29 Aug. 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 ::
2022-06-14 00:08:05 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JMNFN5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMNFN5
Languageen
Target FilePID3771777.pdf
User Groupbalbino@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2015/08.03.22.49 1
sid.inpe.br/banon/2001/03.30.15.38.24 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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