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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M9KS2S
Repositorysid.inpe.br/sibgrapi/2016/08.16.14.19
Last Update2016:08.16.14.19.35 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/08.16.14.19.35
Metadata Last Update2022:05.18.22.21.08 (UTC) administrator
Citation KeyVasconcelosCampNasc:2016:KeDeBa
TitleA Keypoint detector based on Visual and Depth features
FormatOn-line
Year2016
Access Date2024, May 03
Number of Files1
Size5434 KiB
2. Context
Author1 Vasconcelos, Levi Osterno
2 Campos, Mario Fernandes Montenegro
3 Nascimento, Erickson Rangel do
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal de Minas Gerais
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressleviovasconcelos@gmail.com
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2016-08-16 14:19:35 :: leviovasconcelos@gmail.com -> administrator ::
2022-05-18 22:21:08 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordskeypoint detector
RGB-D image
decision tree
information fusion
AbstractOne of the first steps in numerous computer vision tasks is the extraction of keypoints. Despite the large number of works proposing image keypoint detectors, only a few methodologies are able to efficiently use both visual and geometrical information. In this work we introduce KVD (Keypoints from Visual and Depth Data), a novel keypoint detector which is scale invariant and combines intensity and geometrical data using a decision tree. We present results from several experiments showing that our methodology produces the best performing detector when compared to state-of-the-art methods, with the highest repeatability scores for rotations, translations and scale changes, as well as robustness to corrupted visual or geometric data. Additionally, as processing time is concerned, KVD yields the best time performance among methods that also use depth and visual data.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2016 > A Keypoint detector...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M9KS2S
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M9KS2S
Languageen
Target Filecamera-ready-levi.pdf
User Groupleviovasconcelos@gmail.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi 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 versiontype volume


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