Close
Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47QK5P2
Repositorysid.inpe.br/sibgrapi/2022/10.15.16.03
Last Update2022:10.15.16.03.48 (UTC) jordao.bragantini@gmail.com
Metadata Repositorysid.inpe.br/sibgrapi/2022/10.15.16.03.49
Metadata Last Update2023:05.23.04.20.43 (UTC) administrator
Citation KeyBragantiniFalc:2022:GrAlFe
TitleInteractive Image Segmentation: From Graph-based Algorithms to Feature-Space Annotation
FormatOn-line
Year2022
Access Date2024, May 02
Number of Files1
Size6604 KiB
2. Context
Author1 Bragantini, Jordão
2 Falcão, Alexandre Xavier
Affiliation1 Chan Zuckerberge Biohub
2 University of Campinas
e-Mail Addressjordao.bragantini@gmail.com
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
History (UTC)2022-10-15 16:03:49 :: jordao.bragantini@gmail.com -> administrator ::
2023-05-23 04:20:43 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsimage segmentation
interactive image segmentation
data annotation
AbstractIn recent years, machine learning algorithms that solve problems from a collection of examples (i.e. labeled data), have grown to be the predominant approach for solving computer vision and image processing tasks. These algorithms performance is highly correlated with the abundance of examples and their quality, especially methods based on neural networks, which are significantly data-hungry. Notably, image segmentation annotation requires extensive effort to produce high-quality labeling due to the fine-scale of the units (pixels) and resorts to interactive methodologies to provide user assistance. Therefore, improving interactive image segmentation methodologies with the goal of improving data labeling problems is of paramount importance to advance applications of computer vision methods. With this in mind, we investigated the existing literature on interactive image segmentation, contributing to it by introducing novel algorithms that perform the segmentation from markers, contours, and finally proposing a new paradigm for image annotation at scale.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Interactive Image Segmentation:...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 15/10/2022 13:03 1.6 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47QK5P2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47QK5P2
Languageen
Target File2022_Bragantini_WTD_SIBGRAPI-3.pdf
User Groupjordao.bragantini@gmail.com
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume


Close