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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW34M/3JMRSNH
Repositorysid.inpe.br/sibgrapi/2015/06.20.13.19
Last Update2015:06.20.13.19.32 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2015/06.20.13.19.32
Metadata Last Update2022:06.14.00.08.14 (UTC) administrator
DOI10.1109/SIBGRAPI.2015.36
Citation KeySiravenhaCarv:2015:ExUsLe
TitleExploring the Use of Leaf Shape Frequencies for Plant Classification
FormatOn-line
Year2015
Access Date2024, May 06
Number of Files1
Size1373 KiB
2. Context
Author1 Siravenha, Ana Carolina Quintao
2 Carvalho, Schubert Ribeiro
Affiliation1 Federal University of Para
2 Vale Institute of Technology
EditorPapa, Joćo Paulo
Sander, Pedro Vieira
Marroquim, Ricardo Guerra
Farrell, Ryan
e-Mail Addresscarolinaquintao@gmail.com
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)2015-06-20 13:19:32 :: carolinaquintao@gmail.com -> administrator ::
2022-06-14 00:08:14 :: administrator -> :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsPlants classification
Shape features
Fourier transform
Feature selection
AbstractPlant identification and classification play an important role in ecology, but the manual process is cumbersome even for experimented taxonomists. Technological advances allows the development of strategies to make these tasks easily and faster. In this context, this paper describes a methodology for plant identification and classification based on leaf shapes, that explores the discriminative power of the contour-centroid distance in the Fourier frequency domain in which some invariance (e.g. rotation and scale) are guaranteed. In addition, it is also investigated the influence of feature selection techniques regarding classification accuracy. Our results show that by combining a set of features vectors - in the principal components space - and a feedforward neural network, an accuracy of 97.45% was achieved.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2015 > Exploring the Use...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Exploring the Use...
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/3JMRSNH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JMRSNH
Languageen
Target Fileexample_v3.pdf
User Groupcarolinaquintao@gmail.com
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 11
sid.inpe.br/sibgrapi/2022/06.10.21.49 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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