About: Local Binary Pattern Based Features for Sign Language Recognition     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. We compare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very important for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combinations on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99.75% for signer dependent tests and 57.54% for signer independent tests.
  • In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. We compare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very important for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combinations on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99.75% for signer dependent tests and 57.54% for signer independent tests. (en)
Title
  • Local Binary Pattern Based Features for Sign Language Recognition
  • Local Binary Pattern Based Features for Sign Language Recognition (en)
skos:prefLabel
  • Local Binary Pattern Based Features for Sign Language Recognition
  • Local Binary Pattern Based Features for Sign Language Recognition (en)
skos:notation
  • RIV/49777513:23520/11:43898225!RIV12-MSM-23520___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ME08106), S
http://linked.open...iv/cisloPeriodika
  • 3
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 209786
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/11:43898225
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Local Binary Pattern, Sign Language, Sign Language Recognition (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • RU - Ruská federace
http://linked.open...ontrolniKodProRIV
  • [B2023E5FD1B7]
http://linked.open...i/riv/nazevZdroje
  • Pattern Recognition and Image Analysis
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 21
http://linked.open...iv/tvurceVysledku
  • Hrúz, Marek
  • Železný, Miloš
  • Trojanová, Jana
issn
  • 1054-6618
number of pages
http://bibframe.org/vocab/doi
  • 10.1134/S1054661811020416
http://localhost/t...ganizacniJednotka
  • 23520
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 48 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software