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Metadata

1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3A3QGK5
Repositorysid.inpe.br/sibgrapi/2011/07.11.22.08
Last Update2011:07.11.22.08.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2011/07.11.22.08.22
Metadata Last Update2022:06.14.00.07.21 (UTC) administrator
DOI10.1109/SIBGRAPI.2011.9
Citation KeyLaraHira:2011:CoFeCl
TitleCombining features to a class-specific model in an instance detection framework
FormatDVD, On-line.
Year2011
Access Date2024, Apr. 27
Number of Files1
Size3114 KiB
2. Context
Author1 Lara, Arnaldo Câmara
2 Hirata Júnior, Roberto
Affiliation1 Instituto de Matemática e Estatística - Universidade de São Paulo
2 Instituto de Matemática e Estatística - Universidade de São Paulo
EditorLewiner, Thomas
Torres, Ricardo
e-Mail Addressalara@vision.ime.usp.br
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Conference LocationMaceió, AL, Brazil
Date28-31 Aug. 2011
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2011-07-23 15:36:13 :: alara@vision.ime.usp.br -> administrator :: 2011
2022-06-14 00:07:21 :: administrator -> :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsinstance classification
combining features
object model
AbstractObject detection is a Computer Vision task that determines if there is an object of some category (class) in an image or video sequence. When the classes are formed by only one specific object, person or place, the task is known as instance detection. Object recognition classifies an object as belonging to a class in a set of known classes. In this work we deal with an instance detection/recognition task. We collected pictures of famous landmarks from the Internet to build the instance classes and test our framework. Some examples of the classes are: monuments, churches, ancient constructions or modern buildings. We tested several approaches to the problem and a new global feature is proposed to be combined to some widely known features like PHOW. A combination of features and classifiers to model the given instances in the training phase was the most successful one.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2011 > Combining features to...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Combining features to...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3A3QGK5
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3A3QGK5
Languageen
Target File86781.pdf
User Groupalara@vision.ime.usp.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/46SKNPE
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.15.00.56 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage 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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