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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212520%21RIV14-GA0-21230___
rdf:type
skos:Concept n19:Vysledek
rdfs:seeAlso
ftp://cmp.felk.cvut.cz/pub/cmp/articles/bresler/Bresler-Prusa-Hlavac-CVWW-2013.pdf
dcterms:description
This work deals with recognition of hand-drawn graphical symbols in diagrams. We present two contributions. First, we designed a new composite descriptor expressing overall appearance of symbols. We achieved rather favorable accuracy in classification of segmented symbols on benchmark databases, which is 98.93 prec. for a database of flow charts, 98.33 prec. for a database of crisis management icons, and 92.94 perc. for a database of digits. Second, we used the descriptor in the task of simultaneous segmentation and recognition of graphical symbols. Our method creates symbol candidates by grouping spatially close strokes. Symbol candidates are classified by a multiclass SVM classifier learned on a dataset with negative examples. Thus, some portion of the candidates is filtered out. The joint segmentation and classification was tested on diagrams from the flowchart database. We were able to find 91.85 prec. of symbols while generating 8.8 times more symbol candidates than is the number of true symbols per diagram in average. This work deals with recognition of hand-drawn graphical symbols in diagrams. We present two contributions. First, we designed a new composite descriptor expressing overall appearance of symbols. We achieved rather favorable accuracy in classification of segmented symbols on benchmark databases, which is 98.93 prec. for a database of flow charts, 98.33 prec. for a database of crisis management icons, and 92.94 perc. for a database of digits. Second, we used the descriptor in the task of simultaneous segmentation and recognition of graphical symbols. Our method creates symbol candidates by grouping spatially close strokes. Symbol candidates are classified by a multiclass SVM classifier learned on a dataset with negative examples. Thus, some portion of the candidates is filtered out. The joint segmentation and classification was tested on diagrams from the flowchart database. We were able to find 91.85 prec. of symbols while generating 8.8 times more symbol candidates than is the number of true symbols per diagram in average.
dcterms:title
Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor
skos:prefLabel
Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor Simultaneous Segmentation and Recognition of Graphical Symbols using a Composite Descriptor
skos:notation
RIV/68407700:21230/13:00212520!RIV14-GA0-21230___
n19:predkladatel
n20:orjk%3A21230
n3:aktivita
n21:P
n3:aktivity
P(GAP103/10/0783)
n3:dodaniDat
n13:2014
n3:domaciTvurceVysledku
n10:8547777 n10:1542567 n10:4705009
n3:druhVysledku
n4:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
105313
n3:idVysledku
RIV/68407700:21230/13:00212520
n3:jazykVysledku
n22:eng
n3:klicovaSlova
Diagram recognition; Flowchart2076-1465s; Pattern recognition; SVM
n3:klicoveSlovo
n7:Diagram%20recognition n7:Flowchart2076-1465s n7:SVM n7:Pattern%20recognition
n3:kontrolniKodProRIV
[0F64FA33B127]
n3:mistoKonaniAkce
Hernstein
n3:mistoVydani
Vienna
n3:nazevZdroje
CVWW 2013: Proceedings of the 18th Computer Vision Winter Workshop
n3:obor
n17:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n15:GAP103%2F10%2F0783
n3:rokUplatneniVysledku
n13:2013
n3:tvurceVysledku
Bresler, Martin Hlaváč, Václav Průša, Daniel
n3:typAkce
n9:WRD
n3:zahajeniAkce
2013-02-04+01:00
s:numberOfPages
8
n18:hasPublisher
Vienna University of Technology
n12:isbn
978-3-200-02943-9
n6:organizacniJednotka
21230