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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F11%3A43896517%21RIV12-AV0-23520___
rdf:type
skos:Concept n7:Vysledek
dcterms:description
This paper deals with novel automatic categorization of signs used in sign language dictionaries. The categorization provides additional information about lexical signs interpreted in the form of video files. We design a new method for automatic parameterization of these video files and categorization of the signs from extracted information. The method incorporates advanced image processing for detection and tracking of hands and head of signing character in the input image sequences. For tracking of hands we developed an algorithm based on object detection and discriminative probability models. For the tracking of head we use active appearance model. This method is a very powerful for detection and tracking of human face. We specify feasible conditions of the model enabling to use the extracted parameters for basic categorization of the non-manual component. We introduce an experiment with the automatic categorization determining symmetry, location and contact of hands, shape of mouth, close eyes and others. The result of experiment is primary the categorization of more than 200 signs and discussion of problems and next extension. This paper deals with novel automatic categorization of signs used in sign language dictionaries. The categorization provides additional information about lexical signs interpreted in the form of video files. We design a new method for automatic parameterization of these video files and categorization of the signs from extracted information. The method incorporates advanced image processing for detection and tracking of hands and head of signing character in the input image sequences. For tracking of hands we developed an algorithm based on object detection and discriminative probability models. For the tracking of head we use active appearance model. This method is a very powerful for detection and tracking of human face. We specify feasible conditions of the model enabling to use the extracted parameters for basic categorization of the non-manual component. We introduce an experiment with the automatic categorization determining symmetry, location and contact of hands, shape of mouth, close eyes and others. The result of experiment is primary the categorization of more than 200 signs and discussion of problems and next extension.
dcterms:title
Towards Automatic Annotation of Sign Language Dictionary Corpora Towards Automatic Annotation of Sign Language Dictionary Corpora
skos:prefLabel
Towards Automatic Annotation of Sign Language Dictionary Corpora Towards Automatic Annotation of Sign Language Dictionary Corpora
skos:notation
RIV/49777513:23520/11:43896517!RIV12-AV0-23520___
n7:predkladatel
n8:orjk%3A23520
n3:aktivita
n19:S n19:P
n3:aktivity
P(1ET101470416), P(GP102/09/P609), P(ME08106), S
n3:cisloPeriodika
6836
n3:dodaniDat
n12:2012
n3:domaciTvurceVysledku
n11:3572072 n11:4051351 n11:9091424 n11:6895972
n3:druhVysledku
n20:J
n3:duvernostUdaju
n6:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
235613
n3:idVysledku
RIV/49777513:23520/11:43896517
n3:jazykVysledku
n18:eng
n3:klicovaSlova
sign language, computer vision, categorization, annotation
n3:klicoveSlovo
n9:annotation n9:sign%20language n9:computer%20vision n9:categorization
n3:kodStatuVydavatele
DE - Spolková republika Německo
n3:kontrolniKodProRIV
[6241186D90BF]
n3:nazevZdroje
Lecture Notes in Computer Science
n3:obor
n17:JD
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:projekt
n5:ME08106 n5:GP102%2F09%2FP609 n5:1ET101470416
n3:rokUplatneniVysledku
n12:2011
n3:svazekPeriodika
2011
n3:tvurceVysledku
Campr, Pavel Krňoul, Zdeněk Hrúz, Marek Müller, Luděk
s:issn
0302-9743
s:numberOfPages
9
n13:doi
10.1007/978-3-642-23538-2_42
n4:organizacniJednotka
23520