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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3K2HFE8
Repositorysid.inpe.br/sibgrapi/2015/08.06.20.16
Last Update2015:08.06.20.16.39 (UTC) balbson@gmail.com
Metadatasid.inpe.br/sibgrapi/2015/08.06.20.16.39
Metadata Last Update2016:06.03.21.18.38 (UTC) administrator
Citation KeyMesquitaCampNasc:2015:MeObSu
TitleA Methodology for Obtaining Super-Resolution Images and Depth Maps from RGB-D Data
FormatOn-line
Year2015
Access Date2021, Dec. 07
Secondary TypeMaster's Work
Number of Files1
Size2803 KiB
Context area
Author1 Mesquita, Daniel Balbino de
2 Campos, Mario Fernando Montenegro
3 Nascimento, Erickson Rangel do
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorSegundo, Maurício Pamplona
Faria, Fabio Augusto
e-Mail Addressbalbson@gmail.com
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2015-08-06 20:16:39 :: balbson@gmail.com -> administrator ::
2016-06-03 21:18:38 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
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
Target Filesibgrapi_2015_wtd_rgbd_superresolution_camera_ready.pdf
User Groupbalbson@gmail.com
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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